Rapid detection of bleeding before, during, and after fluid resuscitation

ABSTRACT

Novel tools and techniques are provided for assessing, predicting and/or estimating a probability that a patent is bleeding, in some cases, noninvasively. In various embodiments, tools and techniques are provided for implementing rapid detection of bleeding before, during, and after fluid resuscitation, in some instances, in real-time.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit, under 35 U.S.C. § 119(e), of thefollowing co-pending provisional applications: provisional U.S. PatentApplication No. 62/064,809, filed Oct. 16, 2014 by Mulligan et al. andentitled “Rapid Detection of Bleeding During Fluid Resuscitation”(attorney docket no. 0463.14PR, referred to hereinafter as the “'809application”); and provisional U.S. Patent Application No. 62/064,816,filed Oct. 16, 2014 by Mulligan et al. and entitled “Assessing theEffectiveness of CPR” (attorney docket no. 0463.15PR, referred tohereinafter as the “'816 application”), both of which are incorporatedherein by reference. This application may be related to co-pending U.S.patent application Ser. No. ______, filed on a date even herewith byMulligan et al. and entitled “Assessing the Effectiveness of CPR”(attorney docket no. 0463.15), which claims priority to the '809application and the '816 application.

This application a continuation-in-part of the following applications:U.S. patent application Ser. No. 14/542,423, filed Nov. 14, 2014 byMulligan et al. and entitled “Noninvasive Monitoring for FluidResuscitation” (attorney docket no. 0463.11, referred to hereinafter asthe “'423 application”), which claims priority to provisional U.S.Patent Application No. 61/904,436, filed Nov. 14, 2013 by Mulligan etal. and entitled “Noninvasive Monitoring for Fluid Resuscitation”(attorney docket no. 0463.11PR, referred to hereinafter as the “'436application”); and U.S. patent application Ser. No. 14/542,426, filedNov. 14, 2014 by Mulligan et al. and entitled “Noninvasive HydrationMonitoring” (attorney docket no. 0463.12, referred to hereinafter as the“'426 application”), which claims priority to provisional U.S. PatentApplication No. 61/905,727, filed Nov. 18, 2013 by Mulligan et al. andentitled “Noninvasive Hydration Monitoring” (attorney docket no.0463.12PR, referred to hereinafter as the “'727 application”), all ofwhich are incorporated herein by reference.

This application is also a continuation-in-part of U.S. patentapplication Ser. No. 14/535,171, filed Nov. 6, 2014 by Mulligan et al.and entitled, “Noninvasive Predictive and/or Estimative Blood PressureMonitoring” (attorney docket no. 0463.10, referred to hereinafter as the“'171 application”), which claims priority to provisional U.S. PatentApplication No. 61/900,980, filed Nov. 6, 2013 by Mulligan et al. andentitled “Noninvasive Predictive and/or Estimative Blood PressureMonitoring” (attorney docket no. 0463.10PR, referred to hereinafter asthe “'980 application”), both of which are incorporated herein byreference.

This application is also a continuation-in-part of U.S. patentapplication Ser. No. 13/554,483, filed Jul. 20, 2012 by Grudic et al.and entitled, “Hemodynamic Reserve Monitor and Hemodialysis Control”(attorney docket no. 0463.05, referred to hereinafter as the “'483application”), which claims priority to provisional U.S. PatentApplication No. 61/510,792, filed Jul. 22, 2011 by Grudic et al. andentitled “Cardiovascular Reserve Monitor” (attorney docket no.0463.05PR, referred to hereinafter as the “'792 application”), andprovisional U.S. Patent Application No. 61/614,426, filed Mar. 22, 2012by Grudic et al. and entitled “Hemodynamic Reserve Monitor andHemodialysis Control” (attorney docket no. 0463.07PR, referred tohereinafter as the “'426 application”), all of which are herebyincorporated by reference.

The '483 application is also a continuation-in-part of U.S. patentapplication Ser. No. 13/041,006, filed Mar. 4, 2011 by Grudic et al. andentitled “Active Physical Perturbations to Enhance Intelligent MedicalMonitoring” (attorney docket no. 0463.04, referred to hereinafter as the“'006 application”), which claims priority to provisional U.S. PatentApplication No. 61/310,583, filed Mar. 4, 2010, by Grudic and entitled“Active Physical Perturbations to Enhance Intelligent MedicalMonitoring” (attorney docket no. 0463.04PR, referred to hereinafter asthe “'583 application”), both of which are hereby incorporated byreference. The '006 application is a continuation-in-part of U.S. patentapplication Ser. No. 13/028,140 (the “'140 application,” now U.S. Pat.No. 8,512,260), filed Feb. 15, 2011 by Grudic et al. and entitled“Statistical, Noninvasive Measurement of Intracranial Pressure”(attorney docket no. 0463.03) which claims priority to provisional U.S.Patent Application No. 61/305,110, filed Feb. 16, 2010, by Moulton etal. and entitled “Statistical, Noninvasive Method for PredictingIntracranial Pressure” (attorney docket no. 0463.03PR, referred tohereinafter as the “'110 application”), both of which are herebyincorporated by reference.

The '140 application is a continuation in part of InternationalApplication No. PCT/US2009/062119 (the “'119 application”), filed Oct.26, 2009 by Grudic et al. and entitled “Long Term Active Learning fromLarge Continually Changing Data Sets” (attorney docket no. 0463.01/PCT),which claims priority to provisional U.S. Patent Application No.61/252,978 filed Oct. 19, 2009, provisional U.S. Patent Application Nos.61/166,499, 61/166,486, and 61/166,472, filed Apr. 3, 2009, andprovisional U.S. Patent Application No. 61/109,490, filed Oct. 29, 2008,each of which is hereby incorporated by reference.

The respective disclosures of these applications/patents (collectively,the “Related Applications”), which are commonly assigned, areincorporated herein by reference in their entirety for all purposes.

STATEMENT AS TO RIGHTS TO INVENTIONS MADE UNDER FEDERALLY SPONSOREDRESEARCH OR DEVELOPMENT

This invention was made with government support under grant number0535269 awarded by the National Science Foundation; grant numberFA8650-07-C-7702 awarded by the Air Force Research Laboratory; and grantnumbers W81XWH-09-C-1060 and W81XWH-09-1-0750 awarded by Army MedicalResearch Material and Command. The government has certain rights in theinvention.

COPYRIGHT STATEMENT

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

FIELD

The present disclosure relates, in general, tools and techniques formedical monitoring, and more particularly, to tools and techniques thatcan provide rapid detection of bleeding before, during, and after fluidresuscitation.

BACKGROUND

Hemorrhagic shock induced by traumatic injury is a leading cause ofmortality. The first hour following injury has been termed the “goldenhour,” because there is a short interval of time during whichrecognition and proper management of a patient with significant, ongoingbleeding can make the difference between life and death. Significantbleeding is not always clinically evident. Many severely injuredpatients have intracavitary bleeding, which means that bleeding from amajor organ or vessel is contained within the thorax or abdomen. Thereis no external evidence of bleeding and as a result, suspicion andclinical signs of bleeding must be sought by the practitioner. In thefield, where imaging and laboratory tests are generally not available, achange in vital signs over time may be the only indication that apatient is bleeding. Thus, during the “golden hour” one must learn torecognize the signs and symptoms of acute blood loss, then initiatefluid resuscitation and frequently estimate the patient's fluid needs inan ongoing fashion.

The problem is that humans are unable to recognize subtle, beat-to-beatvital sign changes that are indicative of bleeding. More importantly,humans are unable to detect subtle vital sign changes that lead to andare characteristic of impending hemodynamic decompensation orcardiovascular collapse, which is heralded by hypotension withbradycardia.

To further complicate matters, humans have an innate ability tocompensate for significant blood loss with little change in traditionalvital signs. Accordingly, blood loss is difficult to detect usingtraditional vital sign monitoring techniques.

Thus, there is a need for an automated, noninvasive device for earlydiagnosis, real-time monitoring and tracking of blood loss, especiallybefore, during, and after fluid resuscitation procedures.

BRIEF DESCRIPTION OF THE DRAWINGS

A further understanding of the nature and advantages of particularembodiments may be realized by reference to the remaining portions ofthe specification and the drawings, in which like reference numerals areused to refer to similar components. In some instances, a sub-label isassociated with a reference numeral to denote one of multiple similarcomponents. When reference is made to a reference numeral withoutspecification to an existing sub-label, it is intended to refer to allsuch multiple similar components.

FIG. 1A is a schematic diagram illustrating a system for estimatingcompensatory reserve, in accordance with various embodiments.

FIG. 1B is a schematic diagram illustrating a sensor system that can beworn on a patient's body, in accordance with various embodiments.

FIG. 2A is a process flow diagram illustrating a method of assessingblood loss, in accordance with various embodiments.

FIG. 2B illustrates a technique for assessing blood loss, in accordancewith various embodiments.

FIG. 3A is a process flow diagram illustrating a method estimating apatient's compensatory reserve and/or dehydration state, in accordancewith various embodiments.

FIG. 3B illustrates a technique for estimating and/or predicting apatient's compensatory reserve index, in accordance with variousembodiments.

FIG. 4 is a process flow diagram illustrating a method of generating amodel of a physiological state, in accordance with various embodiments.

FIG. 5 is a process flow diagram illustrating a method of implementingrapid detection of bleeding before, during, and after fluidresuscitation, in accordance with various embodiments.

FIGS. 6-8 are exemplary screen captures illustrating display features ofa compensatory reserve monitor showing assessments of blood loss before,during, and/or after fluid resuscitation, in accordance with varioustechniques.

FIGS. 9A-9H are graphical diagrams illustrating rapid detection ofbleeding before, during, and after fluid resuscitation of patients in amulti-trauma clinical study at Denver Health Medical Center, inaccordance with various embodiments.

FIG. 10 is a generalized schematic diagram illustrating a computersystem, in accordance with various embodiments.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

Overview

Various embodiments can detect bleeding before, during, and after fluidresuscitation. In an aspect, such detection can be performednoninvasively. In some embodiments, the detection can be based on acalculation (or estimation) of a patient's compensatory reserve index(“CRI,” also referred to herein and in the Related Applications as“cardiac reserve index” or “hemodynamic reserve index” (“HDRI”)). Inother cases, the assessments might be based on raw waveform data (e.g.,PPG waveform data) captured by a sensor on the patent (such as thesensors described in the Related Applications, for example). In furthercases, a combination of waveform data and calculated/estimated CRI canbe used to calculate the effectiveness of resuscitation and/or theamount of fluid needed for effective resuscitation.

In other aspects, such functionality can be provided by and/orintegrated with systems and devices (such as a cardiac reserve monitor),tool, techniques, methods, and software described in the RelatedApplications, including in particular the '483 Application. For example,various operations described in accordance with the methods disclosed bythe Related Applications can be employed in a method of assessingeffectiveness of resuscitation and/or calculating an amount of fluidneeded for effective resuscitation. Similarly, such techniques can beperformed by the systems and/or embodied by the software productsdescribed in the Related Applications.

An embodiment can include a system that comprises one or more sensorsplaced on the patient and a computer system (such as those described inthe Related Applications) that performs a method for using sensor datafor estimating and predicting (in real-time, after every heartbeat, oras the information is needed) one or more of the relevant parametersoutlined above. Other embodiments can comprise the computer systemprogrammed to perform such a method, an apparatus comprisinginstructions to program a computer to perform such a method, and/or sucha method itself.

A sensor may include but is not limited to any of the following: anoninvasive blood pressure sensor such as the Nexfin (BMEYE, B.V.) orFinometer (Finapres Medical Systems B.V.); invasive arterial bloodpressure, using an arterial catheter; invasive central venous pressure;invasive or noninvasive intracranial pressure monitor; EEG(electroencephalograph); cardiac monitor (EKG); transcranial Dopplersensor; transthoracic impedance plethysmography; pulse oximetry; asensor generating a photoplethysmograph (PPG) waveform; near infraredspectroscopy; electronic stethoscope; and/or the like.

The '809 application describes several exemplary embodiments, butvarious embodiments are not limited to those described in the '809application. For example, FIG. 1 of the '809 application illustrates anexemplary sensor that can be used to collect waveform data for analysis,but other sensors could be used as well. Similarly, the '809 applicationdescribes several techniques for estimating probability of blood loss.Many such techniques depend on an estimate of a patient's CRI, which canbe calculated using the techniques described in the '483 application. Itshould be appreciated, however, that other embodiments of estimating aprobability of bleeding and/or of estimating CRI can be employed invarious embodiments.

Thus, in one aspect, a method can include receiving data from such asensor and analyzing such data using techniques including, but notlimited to, analyzing the data using models described in the RelatedApplications. Merely by way of example, a model might be constructedusing test subject data from a study, such as the LBNP study, which canbe used to predict or estimate a CRI (or HDRI) value, as described inthe Related Applications, and in particular in the '483 application.From this calculated value of CRI (or, in some embodiments, from thewaveform data itself, alone or in combination with the CRI value), aprobability that a patient is bleeding internally before, during, and/orafter fluid resuscitation procedures, for example, using the techniquesdescribed in the '809 application.

For example, in one embodiment, a method might comprise capturingwaveform data from a patient with the sensor before, during, and/orafter fluid resuscitation and/or calculating a CRI value for the patientat these times. In some cases, the variation in CRI values obtainedduring the procedure can be used to estimate a probability that thepatent is bleeding. For instance, the standard deviation of the CRIvalues during the recording and/or the difference in CRI values before,during, and/or after fluid resuscitation can be used to estimateprobability of bleeding, as described more fully with regard to theclinical study detailed in the '809 application.

Some embodiments further comprise normalizing an estimated probabilityof bleeding against a scaling. For example, in some cases, an index from0 to 1 could be used, with 0 indicating that the patient is notbleeding, 1 indicating that the patient is bleeding, and values between0 and 1 indicating relative probabilities that the patient is bleeding,based on the estimates calculated from the CRI values.

The following detailed description illustrates a few exemplaryembodiments in further detail to enable one of skill in the art topractice such embodiments. The described examples are provided forillustrative purposes and are not intended to limit the scope of theinvention. For the purposes of this disclosure, it should be recognizedthat a node could be “virtual” or supported on a hypervisor or Hostsystem, or could be a physical node or network device within a network.In most cases, the figures illustrate bridging a virtual path andpossibly a node (virtual machine) across the path or between twophysical nodes. However, it should be understood that the “swapping” ofpaths via orchestration can occur in any combination of physical and/orvirtual nodes, physical and/or virtual links, or the like.

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the described embodiments. It will be apparent to oneskilled in the art, however, that other embodiments of the presentinvention may be practiced without some of these specific details. Inother instances, certain structures and devices are shown in blockdiagram form. Several embodiments are described herein, and whilevarious features are ascribed to different embodiments, it should beappreciated that the features described with respect to one embodimentmay be incorporated with other embodiments as well. By the same token,however, no single feature or features of any described embodimentshould be considered essential to every embodiment of the invention, asother embodiments of the invention may omit such features.

Unless otherwise indicated, all numbers used herein to expressquantities, dimensions, and so forth used should be understood as beingmodified in all instances by the term “about.” In this application, theuse of the singular includes the plural unless specifically statedotherwise, and use of the terms “and” and “or” means “and/or” unlessotherwise indicated. Moreover, the use of the term “including,” as wellas other forms, such as “includes” and “included,” should be considerednon-exclusive. Also, terms such as “element” or “component” encompassboth elements and components comprising one unit and elements andcomponents that comprise more than one unit, unless specifically statedotherwise.

The tools provided by various embodiments include, without limitation,methods, systems, and/or software products. Merely by way of example, amethod might comprise one or more procedures, any or all of which areexecuted by a computer system. Correspondingly, an embodiment mightprovide a computer system configured with instructions to perform one ormore procedures in accordance with methods provided by various otherembodiments. Similarly, a computer program might comprise a set ofinstructions that are executable by a computer system (and/or aprocessor therein) to perform such operations. In many cases, suchsoftware programs are encoded on physical, tangible, and/ornon-transitory computer readable media (such as, to name but a fewexamples, optical media, magnetic media, and/or the like).

In an aspect, a system might be provided that comprises one or moresensors to obtain physiological data from a patient and a computersystem in communication with the one or more sensors. The computersystem might comprise one or more processors and a non-transitorycomputer readable medium in communication with the one or moreprocessors. The computer readable medium might have encoded thereon aset of instructions executable by the one or more processors to causethe computer system to receive the physiological data from the one ormore sensors, analyze the physiological data, estimate a probabilitythat the patient is bleeding, and display, on a display device, at leastone of an assessment, prediction, or estimate indicating a probabilitythat the patient is bleeding.

In another aspect, a method might be provided that comprises monitoring,with one or more sensors, physiological data of a patient, analyzing,with a computer system, the physiological data, and estimating aprobability that the patient is bleeding, based at least in part on theanalyzed physiological data. The method might further comprisedisplaying, on a display device, an indication of at least one of anassessment, prediction, or estimate of a probability that the patient isbleeding.

In some instances, one or more of monitoring the physiological data,analyzing the physiological data, estimating the probability that thepatient is bleeding, or displaying the indication of at least one of anassessment, prediction, or estimate of the probability that the patientis bleeding are performed in real-time. In some cases, estimating aprobability that the patient is bleeding might comprise estimating aprobability that the patient is bleeding, based on one or more values ofcompensatory reserve index (“CRI”) estimated based on the receivedphysiological data. According to some embodiments, the one or morevalues of CRI are estimated based on physiological data that are atleast one of received before, received during, or received after a fluidresuscitation procedure.

In some embodiments, the one or more values of CRI might comprise aplurality of values of CRI. In some cases, estimating a probability thatthe patient is bleeding might comprise estimating the probability thatthe patient is bleeding based at least in part on one or more of anaverage value of CRI over a particular period of time, a standarddeviation of at least some of the plurality of values of CRI, a skewnessof at least some of the plurality of values of CRI, a rate of change ofat least some of the plurality of values of CRI, a rate of rate changeof at least some of the plurality of values of CRI, and/or a differencebetween at least some of the plurality of values of CRI. In someinstances, the indication is a value between 0 and 1. According to someembodiments, a value of 0 might indicate that the patient is notbleeding, while a value of 1 might indicate that the patient isbleeding.

In some cases, estimating a CRI of the patient comprises estimating acompensatory reserve index by comparing the physiological data to amodel constructed using the following formula:

${{CR{I(t)}} = {1 - \frac{BL{V(t)}}{BLV_{HDD}}}},$

where CRI(t) is the compensatory reserve at time t, BLV(t) is anintravascular volume loss of a test subject at time t, and BLV_(HDD) isan intravascular volume loss at a point of hemodynamic decompensation ofthe test subject. In some embodiments, the physiological data compriseswaveform data and wherein estimating the CRI comprises comparing thewaveform data with one or more sample waveforms generated by exposingone or more test subjects to state of hemodynamic decompensation or nearhemodynamic decompensation, or a series of states progressing towardshemodynamic decompensation, and monitoring physiological data of thetest subjects.

In some instances, the physiological data might comprise waveform data,and estimating the CRI might comprise comparing the waveform data with aplurality of sample waveforms, each of the sample waveformscorresponding to a different value of the CRI to produce a similaritycoefficient expressing a similarity between the waveform data and eachof the sample waveforms, normalizing the similarity coefficients foreach of the sample waveforms, and summing the normalized similaritycoefficients to produce an estimated CRI value for the patient.

According to some embodiments, estimating a probability that the patientis bleeding is based on a fixed time history of monitoring thephysiological data of the patient. Alternatively, estimating aprobability that the patient is bleeding is based on a dynamic timehistory of monitoring the physiological data of the patient. In someinstances, at least one of the one or more sensors is selected from thegroup consisting of a blood pressure sensor, an intracranial pressuremonitor, a central venous pressure monitoring catheter, an arterialcatheter, an electroencephalograph, a cardiac monitor, a transcranialDoppler sensor, a transthoracic impedance plethysmograph, a pulseoximeter, a near infrared spectrometer, a ventilator, an accelerometer,an electrooculogram, a transcutaneous glucometer, an electrolyte sensor,and an electronic stethoscope.

Merely by way of example, in some embodiments, physiological data mightcomprise at least one of blood pressure waveform data, plethysmographwaveform data, or photoplethysmograph (PPG) waveform data.

In some cases, analyzing the physiological data might comprise analyzingthe physiological data against a pre-existing model. In someembodiments, the method might further comprise generating thepre-existing model prior to analyzing the physiological data. In someinstances, generating the pre-existing model might comprise receivingdata pertaining to one or more physiological parameters of a testsubject to obtain a plurality of physiological data sets, directlymeasuring one or more physiological states of the test subject with areference sensor to obtain a plurality of physiological statemeasurements, and correlating the received data with the physiologicalstate measurements of the test subject. According to some embodiments,the one or more physiological states comprises reduced circulatorysystem volume.

In some instances, the method might further comprise inducing thephysiological state of reduced circulatory system volume in the testsubject. In some cases, inducing the physiological state comprise atleast one of subjecting the test subject to lower body negative pressure(“LBNP”), subjecting the test subject to dehydration, and/or the like.In some embodiments, the one or more physiological states might compriseat least one of a state of cardiovascular collapse ornear-cardiovascular collapse, a state of euvolemia, a state ofhypervolemia, a state of dehydration, and/or the like.

According to some embodiments, correlating the received data with thephysiological state measurements of the test subject might compriseidentifying a most predictive set of signals S_(k) out of a set ofsignals s₁, s₂, . . . , s_(D) for each of one or more outcomes o_(k),autonomously learning a set of probabilistic predictive modelsô_(k)=M_(k)(S_(k)), and repeating the operation of autonomously learningincrementally from data that contains examples of values of signals s₁,s₂, . . . , s_(D) and corresponding outcomes o₁, o₂, . . . , o_(K).Here, the most-predictive set of signals S_(k) corresponds to a firstdata set representing a first physiological parameter, and each of theone or more outcomes o_(k) represents a physiological state measurement,while ô_(k) is a prediction of outcome o_(k) derived from a model M_(k)that uses as inputs values obtained from the set of signals S_(k).

In yet another aspect, an apparatus might be provided that comprises anon-transitory computer readable medium that has encoded thereon a setof instructions executable by one or more computers to cause theapparatus to receive physiological data from one or more sensors,analyze the physiological data, estimate a probability that the patientis bleeding, and display, on a display device, at least one of anassessment, prediction, or estimate indicating a probability that thepatient is bleeding.

Various modifications and additions can be made to the embodimentsdiscussed without departing from the scope of the invention. Forexample, while the embodiments described above refer to particularfeatures, the scope of this invention also includes embodiments havingdifferent combination of features and embodiments that do not includeall of the above described features.

Compensatory Reserve Index (“CRI”)

Various embodiments can assess the effectiveness of fluid intakehydration, where effectiveness can be defined, but not limited to, asleading to a better hydration state or maintain an optimal hydrationstate. In one aspect, optimal hydration might be defined as a fluidstate that maximized some performance index/measure, perhaps indicatedby the patient's compensatory reserve index (“CRI,” also referred toherein and in the Related Applications as “cardiac reserve index” or“hemodynamic reserve index” (“HDRI”), all of which should be consideredsynonymous for purposes of this disclosure). (While the term, “patient,”is used herein for convenience, that descriptor should not be consideredlimiting, because various embodiments can be employed both in a clinicalsetting and outside any clinical setting, such as by an athlete before,during, or after an athletic contest or training, a person during dailyactivities, a soldier on the battlefield, etc. Thus, the term,“patient,” as used herein, should be interpreted broadly and should beconsidered to be synonymous with “person.”) In other cases, theassessments might be based on raw waveform data (e.g., PPG waveformdata) captured by a sensor on the patent (such as the sensors describedbelow and the Related Applications, for example). In further cases, acombination of waveform data and calculated/estimated CRI can be used tocalculate the effectiveness of hydration and/or the amount of fluidneeded for effective hydration. In other aspects, such functionality canbe provided by and/or integrated with systems, devices (such as acardiac reserve monitor and/or wrist-worn sensor device), tools,techniques, methods, and software described below and in the RelatedApplications.

For example, one set of embodiments provides methods. An exemplarymethod might comprise monitoring, with one or more sensors,physiological data of a patient. The method might further compriseanalyzing, with a computer system, the physiological data. Manydifferent types of physiological data can be monitored and/or analyzedby various embodiments, including without limitation, blood pressurewaveform data, plethysmograph waveform data, photoplethysmograph (“PPG”)waveform data (such as that generated by a pulse oximeter), and/or thelike. In an aspect of some embodiments, analyzing the physiological datamight comprise analyzing the data against a pre-existing model. In somecases, the method can further comprise assessing the effectiveness ofhydration efforts, and/or displaying (e.g., on a display device) anassessment of the effectiveness of the hydration efforts. Such anassessment can include, without limitation, an estimate of theeffectiveness at a current time, a prediction of the effectiveness atsome point in the future, an estimate and/or prediction of a volume offluid necessary for effective hydration, an estimate of the probabilitya patient requires fluids, etc.

An apparatus, in accordance with yet another set of embodiments, mightcomprise a computer readable medium having encoded thereon a set ofinstructions executable by one or more computers to perform one or moreoperations. In some embodiments, the set of instructions might compriseinstructions for performing some or all of the operations of methodsprovided by certain embodiments.

A system, in accordance with yet another set of embodiments, mightcomprise one or more processors and a computer readable medium incommunication with the one or more processors. The computer readablemedium might have encoded thereon a set of instructions executable bythe computer system to perform one or more operations, such as the setof instructions described above, to name one example. In someembodiments, the system might further comprise one or more sensorsand/or a therapeutic device, either or both of which might be incommunication with the processor and/or might be controlled by theprocessor. Such sensors can include, but are not limited to, a bloodpressure sensor, an intracranial pressure monitor, a central venouspressure monitoring catheter, an arterial catheter, anelectroencephalograph, a cardiac monitor, a transcranial Doppler sensor,a transthoracic impedance plethysmograph, a pulse oximeter, a nearinfrared spectrometer, a ventilator, an accelerometer, anelectrooculogram, a transcutaneous glucometer, an electrolyte sensor,and/or an electronic stethoscope.

CRI for Assessing Blood Loss Before, During, and/or After FluidResuscitation

A set of embodiments provides methods, systems, and software that can beused, in many cases noninvasively, to quickly and accurately assessblood loss in a patient (e.g., before, during, and/or after fluidresuscitation). Such an assessment can include, without limitation, anestimate of the effectiveness at a current time, a prediction of theeffectiveness at some point in the future, an estimate and/or predictionof a volume of fluid necessary for effective hydration, an estimate ofthe probability a patient requires fluids, an estimate and/or predictionof blood loss (e.g., before, during, and/or after fluid resuscitation),etc. In a particular set of embodiments, a device, which can be worn onthe patient's body, can include one or more sensors that monitor apatient's physiological parameters. The device (or a computer incommunication with the device) can analyze the data captured by thesensors and compare such data with a model (which can be generated inaccordance with other embodiments) to assess the effectiveness ofhydration, as described in further detail in the '426 application,and/or to assess blood loss (e.g., before, during, and/or after fluidresuscitation).

Different embodiments can measure a number of different physiologicalparameters from the patient, and the analysis of those parameters canvary according to which parameters are measured (and which, according tothe generated model, are found to be most predictive of theeffectiveness of hydration, including the probability of the need forhydration and/or the volume of fluids needed, or most predictive ofblood loss). In some cases, the parameters themselves (e.g., continuouswaveform data captured by a photoplethysmograph) can be analyzed againstthe model to make assessments of hydration effectiveness or assessmentsof blood loss (e.g., before, during, and/or after fluid resuscitation).In other cases, physiological parameters can be derived from thecaptured data, and these parameters can be used Merely by way ofexample, as described further below and the '483 application (alreadyincorporated by reference), direct physiological data (captured bysensors) can be used to estimate a value of CRI, and this value of CRIcan be used to assess the effectiveness of hydration and/or to assessblood loss (e.g., before, during, and/or after fluid resuscitation). Inyet other cases, the derived CRI values and raw sensor data can be usedtogether to perform such assessments.

For example, the '483 application describes a compensatory reservemonitor (also described as a cardiac reserve monitor or hemodynamicreserve monitor) that is able to estimate the compensatory reserve of apatient. In an aspect, this monitor quickly, accurately and/or inreal-time can determine the probability of whether a patient isbleeding. In another aspect, the device can simultaneously monitor thepatient's compensatory reserve by tracking the patient's CRI, toappropriately and effectively guide hydration and ongoing patient care.The same device (or a similar device) can also include advancedfunctionality to assess the effectiveness of hydration, based on themonitored CRI values, as explained in further detail in the '426application, and/or to rapidly assess blood loss (e.g., before, during,and/or after fluid resuscitation).

CRI is a hemodynamic parameter that is indicative of theindividual-specific proportion of intravascular fluid reserve remainingbefore the onset of hemodynamic decompensation. CRI has values thatrange from 1 to 0, where values near 1 are associated with normovolemia(normal circulatory volume) and values near 0 are associated with theindividual specific circulatory volume at which hemodynamicdecompensation occurs.

The mathematical formula of CRI, at some time “t” is given by thefollowing equation:

$\begin{matrix}{{CR{I(t)}} = {1 - \frac{BL{V(t)}}{BLV_{HDD}}}} & \left( {{Eq}.1} \right)\end{matrix}$

Where BLV(t) is the intravascular volume loss (“BLV,” also referred toas “blood loss volume” in the Related Applications) of a person at time“t,” and BLV_(HDD) is the intravascular volume loss of a person whenthey enter hemodynamic decompensation (“HDD”). Hemodynamicdecompensation is generally defined as occurring when the systolic bloodpressure falls below 70 mmHg. This level of intravascular volume loss isindividual specific and will vary from subject to subject.

Lower body negative pressure (LBNP) in some linear or nonlinearrelationship k with intravascular volume loss:

BLV=λ·LBNP  (Eq. 2)

can be used in order to estimate the CRI for an individual undergoing aLBNP experiment as follows:

$\begin{matrix}{{CRI} = {{{1 - \frac{BL{V(t)}}{BLV_{HDD}}} \approx {1 - \frac{{\lambda \cdot L}BN{P(t)}}{{\lambda \cdot L}BNP_{HDD}}}} = {1 - \frac{LBN{P(t)}}{LBNP_{HDD}}}}} & \left( {{Eq}.3} \right)\end{matrix}$

Where LBNP(t) is the LBNP level that the individual is experiencing attime “t,” and, LBNP_(HDD) is the LNPB level that the individual willenter hemodynamic decompensation.

Using either CRI data, raw (or otherwise processed) sensor data, orboth, various embodiments can assess the effectiveness of hydration. Inone embodiment, the assessment of blood loss (“BL”) can be expressed asa value between 0 and 1; when BL=1, blood loss is certain, when BL=0,there is no blood loss, and when BL is a value between 1 and 0, thevalue is indicative of probability of blood loss, perhaps due to ongoingbleeding before, during, and/or after fluid resuscitation. (Of course,other embodiments can scale the value of BL differently). In an aspectof some embodiments, a general expression for the estimate of asfollows:

BL=ƒ _(BL)(CRI _(t) ,FV _(t) ,S _(t))  (Eq. 4)

Where BL is a measure or an estimate of blood loss,ƒ_(BL)(CRI_(t),FV_(t),S_(t)) is an algorithm embodied by a modelgenerated empirically, e.g., using the techniques described with respectto FIG. 4 below, and/or in the Related Applications, CRI_(t) is a timehistory of CRI values (which can range from a single CRI value to manyhours of CRI values), FV_(t) is a time history of fluid volume beinggiven to the patient (which can range from a single value to many hoursof values), and S_(t) is a time history of raw sensor values, such asphysiological data measured by the sensors, as described elsewhereherein (which can range from one value to many hours of values).

The functional form of Eq. 4 is similar to but not limited to the formof the CRI model in the sense that time histories of(CRI_(t),FV_(t),S_(t)) data gathered from human subjects at variouslevels of BL are compared to time histories of (CRI_(t),FV_(t),S_(t))for the current patient being monitored. The estimated BL for thecurrent patient is then that which is the closest in(CRI_(t),FV_(t),S_(t)) space to the previously gathered data.

While Eq. 4 is the general expression for BL, various embodiments mightuse subsets of the parameters considered in Eq. 4. For instance, in oneembodiment, a model might consider only the volume of fluid and CRIdata, without accounting for raw sensor input. In that case, BL can becalculated as follows:

BL=ƒ _(BL)(CRI _(t) ,FV _(t))  (Eq. 5)

Similarly, some models might estimate BL based on sensor data, ratherthan first estimating CRI, in which case, BL can be expressed thusly:

BL=ƒ _(BL)(FV _(t) ,S _(t))  (Eq. 6)

The choice of parameters to use in modeling BL is discretionary, and itcan depend on what parameters are shown (e.g., using the techniques ofFIG. 4 , below) to result in the best prediction of BL.

In another aspect, the effectiveness of hydration can be assessed byestimating or predicting the volume, V, of fluid necessary for effectivehydration of the patient. This volume, V, can indicate a volume of fluidneeded for full hydration if therapy has not yet begun, and/or it canindicate a volume remaining for fully effective hydration if therapy isunderway. Like BL, the value of V can be estimated/predicted using themodeling techniques described herein and in the Related Applications. Ina general case, V can be expressed as the following:

V=ƒ _(V)(CRI _(t) ,FV _(t) ,S _(t))  (Eq. 7)

where V is an estimated volume of fluid needed by a patient need toprevent over or under hydration, ƒ_(V)(CRI_(t),FV_(t),S_(t)) is analgorithm embodied by a model generated empirically, e.g., using thetechniques described with respect to FIG. 4 below, and/or in the RelatedApplications, CRI_(t) is a time history of CRI values, FV_(t) is a timehistory of fluid volume being given to the patient, and S_(t) is a timehistory of physiological data received from the one or more sensors.

As with the estimate of BL, various embodiments can employ subsets ofthe parameters used in the general expression of Eq.7. Thus, differentembodiments might calculate V as follows:

V=ƒ _(V)(CRI _(t) ,FV _(t))  (Eq. 8)

or

V=ƒ _(V)(FV _(t) ,S _(t))  (Eq. 9)

Yet another way of assessing effectiveness of hydration (which can eveninclude assessing the need for hydration) is estimating the probabilityP_(ƒ) that the patient requires fluids; this probability can estimatethe likelihood that the patient requires hydration if therapy has notbeen initiated, and/or, if hydration therapy is underway, theprobability can estimate the likelihood that further hydration isnecessary. The value of this probability, which can be expressed, e.g.,as a percentage, as a decimal value between 0 and 1, etc. can beestimated using the following expression:

P _(ƒ)=ƒ_(P) _(ƒ) (CRI _(t) ,S _(t))  (Eq. 10)

where P_(ƒ) is the estimated probability that the patient requiresfluid, ƒ_(P) _(ƒ) (CRI_(t),S_(t)) is a relationship derived based onempirical study, CRI_(t) is a time history of CRI values, and S_(t) is atime history of physiological data received from the one or moresensors. Once again, this general expression can be employed, in variousembodiments, using subsets of the parameters in the general expression,such as the following:

P _(ƒ)=ƒ_(P) _(ƒ) (CRI _(t))  (Eq. 11)

or

P _(ƒ)=ƒ_(P) _(ƒ) (S _(t))  (Eq. 12)

In the estimate of any of BL, V, or P_(ƒ), the function ƒ expresses arelationship that is derived based on empirical study. In a set ofembodiments, for example, various sensor data can be collected from testsubjects before, during, and/or after hydration efforts, duringhemorrhaging, or under other conditions that might simulate suchsituations. This sensor data can be analyzed to develop models, usingtechniques similar to those of FIG. 4 below, which can then be used toestimate various assessments of hydration effectiveness, using, e.g.,the methods described below with respect to FIGS. 2 and 3 .

A measure of CRI, BL, V, and/or P_(ƒ) can be useful in a variety ofclinical settings, including but not limited to: 1) acute blood lossvolume due to injury or surgery; 2) acute circulatory volume loss due tohemodialysis (also called intradialytic hypotension); and 3) acutecirculatory volume loss due to various causes of dehydration (e.g.reduced fluid intake, vomiting, dehydration, etc.). A change in CRI canalso herald other conditions, including without limitation changes inblood pressure, general fatigue, overheating and certain types ofillnesses. Accordingly, the tools and techniques for estimating and/orpredicting CRI can have a variety of applications in a clinical setting,including without limitation diagnosing such conditions.

Moreover, measures of CRI, BL, V, and/or P_(ƒ) can have applicabilityoutside the clinical setting. For example, an athlete can be monitored(e.g., using a wrist-wearable hydration monitor) before, during, orafter competition or training to ensure optimal performance (and overallhealth and recovery). In other situations, a person concerned aboutoverall wellbeing can employ a similar hydration monitor to ensure thathe or she is getting enough (but not too much) fluid, ill infants oradults can be monitored while ill to ensure that symptoms (e.g.,vomiting, diarrhea) do not result in dehydration, and the like.Similarly, soldiers in the field (particularly in harsh conditions) canbe monitored to ensure optimal operational readiness.

In various embodiments, a hydration monitor, compensatory reservemonitor, a wrist-wearable sensor device, and/or another integratedsystem can include, but is not limited to, some or all of the followingfunctionality, as described in further detail herein and in the RelatedApplications:

A. Estimating and/or displaying intravascular volume loss to hemodynamicdecompensation (or cardiovascular collapse).

B. Estimating, predicting and/or displaying a patient's compensatoryreserve as an index that is proportional to an approximate measure ofintravascular volume loss to CV collapse, recognizing that each patienthas a unique reserve capacity.

C. Estimating, predicting and/or displaying a patient's compensatoryreserve as an index with a normative value at euvolemia (for example,CRI=1), representing a state in which the patient is normovolemic; aminimum value (for example, CRI=0) which implies no circulatory reserveand that the patient is experiencing CV collapse; and/or an excess value(for example, CRI>1) representing a state in which the patient ishypervolemic; the patient's normalized compensatory reserve can bedisplayed on a continuum between the minimum and maximum values (perhapslabeled by different symbols and/or colors depending on where thepatient falls on the continuum).

D. Determining and/or displaying a probability that bleeding orintravascular volume loss has occurred.

E. Displaying an indicator that intravascular volume loss has occurredand/or is ongoing; as well as other measures of reserve, such as trendlines.

F. Estimating a patient's current blood pressure and/or predicting apatient's future blood pressure.

G. Estimating the current effectiveness of fluid resuscitation efforts.

H. Predicting the future effectiveness of fluid resuscitation efforts.

I. Estimating and/or predicting a volume of fluid necessary foreffective resuscitation.

J. Estimating a probability that a patient needs fluids.

K. Estimating a hydration state of a patient or user.

L. Predicting a future hydration state of a patient or user.

M. Estimate and/or predicting a volume of fluid intake necessary foradequate hydration of a patient or user.

N. Estimating a probability that a patient is dehydrated.

In various embodiments, CRI, BL, V, and/or Pf estimates can be (i) basedon a fixed time history of patient monitoring (for example a 30 secondor 30 heart beat window); (ii) based on a dynamic time history ofpatient monitoring (for example monitoring for 200 minutes, the systemmay use all sensor information gathered during that time to refine andimprove CRI estimates, hydration effectiveness assessments, etc.); (iii)based on either establishing baseline estimates when the patient isnormovolemic (no volume loss has occurred); and/or (iv) based on NObaselines estimates when patient is normovolemic.

Certain embodiments can also recommend treatment options, based on theanalysis of the patient's condition (including the estimated/predictedblood pressure, probability of bleeding, state of dehydration, and/orthe patient's estimated and/or predicted CRI). Treatment options caninclude, without limitation, such things as optimizing hemodynamics,ventilator adjustments, IV fluid adjustments (e.g., controlling the flowrate of an IV pump or the drip rate of an IV drip), transfusion of bloodor blood products, infusion of volume expanders, medication changes,changes in patient position and surgical therapy.

As one example, certain embodiments can be used to control an IV drip,IV pump, or rapid infuser. For instance, an embodiment might estimatethe probability that a patient requires fluids and activate such adevice in response to that estimate (or instruct a clinician to attachsuch a device to the patient and activate the device). The system mightthen monitor the progress of the hydration effort (through continual orperiodic assessment of the effectiveness of hydration) andincrease/decrease drip or flow rates accordingly.

As another example, certain embodiments can be used as an input for ahemodialysis procedure. For example, certain embodiments can predict howmuch intravascular (blood) volume can be safely removed from a patientduring a hemodialysis process. For example, an embodiment might provideinstructions to a human operator of a hemodialysis machine, based onestimates or predictions of the patient's CRI. Additionally and/oralternatively, such embodiments can be used to continuously self-adjustthe ultra-filtration rate of the hemodialysis equipment, therebycompletely avoiding intradialytic hypotension and its associatedmorbidity.

As yet another example, certain embodiments can be used to estimateand/or predict a dehydration state (and/or the amount of dehydration) inan individual (e.g., a trauma patient, an athlete, an elder living athome, etc.) and/or to provide treatment (either by providingrecommendations to treating personnel or by directly controllingappropriate therapeutic equipment). For instance, if an analytical modelindicates a relationship between CRI (and/or any other physiologicalphenomena that can be measured and/or estimated using the techniquesdescribed herein and in the Related Applications) and dehydration state,an embodiment can apply that model, using the techniques describedherein, to estimate a dehydration state of the patient.

Specific Exemplary Embodiments

We now turn to the embodiments as illustrated by the drawings. FIGS.1-10 illustrate some of the features of the method, system, andapparatus for implementing rapid detection of bleeding before, during,and after fluid resuscitation, as referred to above. FIGS. 1-8illustrate some of the specific (although non-limiting) exemplaryfeatures of the method, system, and apparatus for implementing rapiddetection of bleeding before, during, and after fluid resuscitation,while FIGS. 9A-9H illustrate implementing rapid detection of bleedingbefore, during, and after fluid resuscitation of patients in a clinicaltrial, and FIG. 10 illustrates exemplary system and hardwareimplementation. The methods, systems, and apparatuses illustrated byFIGS. 1-10 refer to examples of different embodiments that includevarious components and steps, which can be considered alternatives orwhich can be used in conjunction with one another in the variousembodiments. The description of the illustrated methods, systems, andapparatuses shown in FIGS. 1-10 is provided for purposes of illustrationand should not be considered to limit the scope of the differentembodiments.

With reference to the figures, FIG. 1A provides a general overview of asystem provided by certain embodiments. The system includes a computersystem or computational device 100 in communication with one or moresensors 105, which are configured to obtain physiological data from thesubject (e.g., animal or human test subject or patient) 110. In oneembodiment, the computer system 100 comprises a Lenovo THINKPAD X200, 4GB of RAM with Microsoft WINDOWS 7 operating system and is programmedwith software to execute the computational methods outlined herein. Thecomputational methods can be implemented in MATLAB 2009b and C++programming languages. A more general example of a computer system 100that can be used in some embodiments is described in further detailbelow. Even more generally, however, the computer system 100 can be anysystem of one or more computers that are capable of performing thetechniques described herein. In a particular embodiment, for example,the computer system 100 is capable of reading values from thephysiological sensors 105, generating models of physiological state fromthose sensors, and/or employing such models to make individual-specificestimations, predictions, or other diagnoses, displaying the results,recommending and/or implementing a therapeutic treatment as a result ofthe analysis, and/or archiving (learning) these results for use infuture, model building and predictions.

The sensors 105 can be any of a variety of sensors (including withoutlimitation those described herein) for obtaining physiological data fromthe subject. An exemplary sensor suite might include a Finometer sensorfor obtaining a noninvasive continuous blood pressure waveform, a pulseoximeter sensor, an Analog to Digital Board (National InstrumentsUSB-9215A 16-Bit, 4 channel) for connecting the sensors (either thepulse oximeter and/or the finometer) to the computer system 100. Moregenerally, in an embodiment one or more sensors 105 might obtain, e.g.,using one or more of the techniques described herein, continuousphysiological waveform data, such as continuous blood pressure. Inputfrom the sensors 105 can constitute continuous data signals and/oroutcomes that can be used to generate, and/or can be applied to, apredictive model as described below.

In some cases, the structure or system might include a therapeuticdevice 115 (also referred to herein as a “physiological assistivedevice”), which can be controlled by the computer system 100 toadminister therapeutic treatment, in accordance with the recommendationsdeveloped by analysis of a patient's physiological data. In a particularembodiment, the therapeutic device might comprise hemodialysis equipment(also referred to as a hemodialysis machine), which can be controlled bythe computer system 100 based on the estimated CRI of the patient, asdescribed in further detail below. Further examples of therapeuticdevices in other embodiments can include a cardiac assist device, aventilator, an automatic implantable cardioverter defibrillator(“AICD”), pacemakers, an extracorporeal membrane oxygenation circuit, apositive airway pressure (“PAP”) device (including without limitation acontinuous positive airway pressure (“cPAP”) device or the like), ananesthesia machine, an integrated critical care system, a medical robot,intravenous and/or intra-arterial pumps that can provide fluids and/ortherapeutic compounds (e.g., through intravenous injection), intravenousdrips, a rapid infuser, a heating/cooling blanket, and/or the like.

FIG. 1B illustrates in more detail an exemplary sensor device 105, whichcan be used in the system 100 described above. (It should be noted, ofcourse, that the depicted sensor device 105 of FIG. 1B is not intendedto be limiting, and different embodiments can employ any sensor thatcaptures suitable data, including, without limitation, sensors describedelsewhere in this disclosure and in the Related Applications.) Theillustrated sensor device 105 is designed to be worn on a patient'swrist and therefore can be used both in clinical settings and in thefield (e.g., on any person for whom monitoring might be beneficial, fora variety of reasons, including without limitation assessment of bloodpressure and/or hydration during athletic competition or training, dailyactivities, military training or action, etc.). In one aspect, thesensor device 105 can serve as an integrated hydration monitor, whichcan assess hydration as described herein, display an indication of theassessment, recommend therapeutic action based on the assessment, or thelike, in a form factor that can be worn during athletic events and/ordaily activities.

Hence, the exemplary sensor 105 device (hydration monitor) includes afinger cuff 125 and a wrist unit 130. The finger cuff 125 includes afingertip sensor 135 (in this case, a PPG sensor) that captures databased on physiological conditions of the patient, such as PPG waveformdata. The sensor 135 communicates with an input/output unit 140 of thewrist unit 130 to provide output from the sensor 135 to a processingunit 145 of the wrist unit 130. Such communication can be wired (e.g.,via a standard—such as USB—or proprietary connector on the wrist unit130) and/or wireless (e.g., via Bluetooth, such as Bluetooth Low Energy(“BTLE”), near field connection (“NFC”), WiFi, or any other suitableradio technology).

In different embodiments, the processing unit 145 can have differenttypes of functionality. For example, in some cases, the processing unit145 might simply act to store and/or organize data prior to transmittingthe data through the I/O unit 140 to a monitoring computer 100, whichmight perform data analysis, to control a therapeutic device 115, etc.In other cases, however, the processing unit 145 might act as aspecialized computer (e.g., with some or all of the components describedin connection with FIG. 10 , below and/or some or all of thefunctionality ascribed to the computer 100 of FIGS. 1A and 1B), suchthat the processing unit 145 can perform data analysis onboard, e.g., toestimate and/or predict a patient's current and/or future bloodpressure. As such, the wrist unit 105 might include a display, which candisplay any output described herein, including, without limitation,estimated and/or predicted values (e.g., of CRI, blood pressure,hydration status, etc.), data captured by the sensor (e.g., heart rate,pulse ox, etc.), and/or the like.

In some cases, the wrist unit 130 might include a wrist strap 155 thatallows the unit to be worn on the wrist, similar to a wrist watch. Ofcourse, other options are available to facilitate transportation of thesensor device 105 with a patent. More generally, the sensor device 105might not include all of the components described above, and/or variouscomponents might be combined and/or reorganized; once again, theembodiment illustrated by FIG. 1B should be considered onlyillustrative, and not limiting, in nature.

FIGS. 2A, 2B, 3A, 3B, 4, and 5 illustrate methods and screen displays inaccordance with various embodiments. While the methods of FIGS. 2A, 2B,3A, 3B, 4 , and 5 are illustrated, for ease of description, as differentmethods, it should be appreciated that the various techniques andprocedures of these methods can be combined in any suitable fashion, andthat, in some embodiments, the methods depicted by FIGS. 2A, 2B, 3A, 3B,4, and 5 can be considered interoperable and/or as portions of a singlemethod. Similarly, while the techniques and procedures are depictedand/or described in a certain order for purposes of illustration, itshould be appreciated that certain procedures may be reordered and/oromitted within the scope of various embodiments. Moreover, while themethods illustrated by FIGS. 2A, 2B, 3A, 3B, 4, and 5 can be implementedby (and, in some cases, are described below with respect to) thecomputer system 100 of FIG. 1 (or other components of the system, suchas the sensor 105 of FIGS. 1A and 1B), these methods may also beimplemented using any suitable hardware implementation. Similarly, whilethe computer system 100 of FIG. 1 (and/or other components of such asystem) can operate according to the methods illustrated by FIGS. 2A,2B, 3A, 3B, 4, and 5 (e.g., by executing instructions embodied on acomputer readable medium), the system 100 can also operate according toother modes of operation and/or perform other suitable procedures.

Merely by way of example, a method might comprise one or moreprocedures, any or all of which are executed by a computer system.Correspondingly, an embodiment might provide a computer systemconfigured with instructions to perform one or more procedures inaccordance with methods provided by various other embodiments.Similarly, a computer program might comprise a set of instructions thatare executable by a computer system (and/or a processor therein) toperform such operations. In many cases, such software programs areencoded on physical, tangible and/or non-transitory computer readablemedia (such as, to name but a few examples, optical media, magneticmedia, and/or the like).

By way of non-limiting example, various embodiments can comprise amethod for using sensor data to assess blood loss in a patient. FIG. 2illustrates an exemplary method 200 in accordance with variousembodiments. The method 200 might comprise generating a model, e.g.,with a computer system, against which patient data can be analyzed toestimate and/or predict various physiological states (block 205). In ageneral sense, generating the model can comprise receiving datapertaining to a plurality of more physiological parameters of a testsubject to obtain a plurality of physiological data sets. Such data caninclude PPG waveform data to name one example, and/or any other type ofsensor data including, without limitation, data captured by othersensors described herein and in the Related Applications.

Generating a model can further comprise directly measuring one or morephysiological states of the test subject with a reference sensor toobtain a plurality of physiological state measurements. The one or morephysiological states can include, without limitation, states of variousvolumes of blood loss and/or fluid resuscitation, and/or various statesof hydration and/or dehydration. (In other embodiments, different statescan include a state of hypervolemia, a state of euvolemia, and/or astate of cardiovascular collapse (or near-cardiovascular collapse),and/or can include states that have been simulated, e.g., through use ofan LBNP apparatus). Other physiological states that can be used togenerate a model are described elsewhere herein and in the RelatedApplications.

Generating the model can further comprise correlating the physiologicalstate(s) with the measured physiological parameters. There are a varietyof techniques for generating a model in accordance with differentembodiments, using these general functions. One exemplary technique forgenerating a model of a generic physiological state is described belowwith respect to FIG. 4 , below, which provides a technique using amachine-learning algorithm to optimize the correlation between measuredphysiological parameters (such as PPG waveform data, to name oneexample) and physical states (e.g., various blood volume states,including states where a known volume of blood loss has occurred and/ora known volume of fluid resuscitation has been administered, variousstates of hydration and/or dehydration, etc.). It should be appreciated,however, that any suitable technique or model may be employed inaccordance with various embodiments.

A number of physiological states can be modeled, and a number ofdifferent conditions can be imposed on test subjects as part of themodel generation. For example, physiological states that can be induced(or monitored when naturally occurring) in test subjects include,without limitation, reduced circulatory system volume, known volume ofblood loss, specified amounts of fluids added to blood volume,dehydration, cardiovascular collapse or near-cardiovascular collapse,euvolemia, hypervolemia, low blood pressure, high blood pressure, normalblood pressure, and/or the like.

Merely by way of example, in one set of embodiments, a number ofphysiological parameters of a plurality of test subjects might bemeasured. In some cases, a subject might undergo varying, measuredlevels of blood loss (either real or simulated) or intravenous fluidaddition. Using the method described below with respect to FIG. 4 (orother, similar techniques, many of which are described in the RelatedApplications), the system can determine which sensor information mosteffectively differentiates between subjects at different bloodloss/addition volume levels.

Additionally and/or alternatively to using direct (e.g., raw) sensordata to build such models, some embodiments might construct a modelbased on data that is derived from sensor data. Merely by way ofexample, one such model might use, as input values, CRI values of testsubjects in different blood loss and/or volume addition conditions.Accordingly, the process of generating a model might first comprisebuilding a model of CRI, and then, from that model, building a model ofhydration effectiveness. (In other cases, a hybrid model might considerboth raw sensor data and CRI data.)

A CRI model can be generated in different ways. For example, in somecases, one or more test subjects might be subjected to LBNP. In anexemplary case, LBNP data is collected from human subjects being exposedto progressively lower levels of LBNP, until hemodynamic decompensation,at which time LBNP is released and the subject recovers. Each level ofLBNP represents an additional amount of blood loss. During these tests,physiological data (including, without limitation, waveform data, suchas continuous non-invasive blood pressure data) can be collected before,during, and/or after the application of the LBNP. As noted above, arelationship (as expressed by Equation 2) can be identified between LBNPand intravascular volume loss, and this relationship can be used toestimate CRI. Hence, LBNP studies form a framework (methodology) for thedevelopment of the hemodynamic parameter referred to herein as CRI andcan be used to generate models of this parameter.

More generally, several different techniques that induce a physiologicalstate of reduced volume in the circulatory system, e.g., to a point ofcardiovascular collapse (hemodynamic decompensation) or to a point nearcardiovascular collapse, can be used to generate such a model. LBNP canbe used to induce this condition, as noted above. In some cases, such asin a study described below, dehydration can be used to induce thiscondition as well. Other techniques are possible as well. Similarly,data collected from a subject in a state of euvolemia, dehydration,hypervolemia, and/or other states might be used to generate a CRI modelin different embodiments.

At block 210, the method 200 comprises monitoring, with one or moresensors, physiological data of a patient. As noted above, a variety ofphysical parameters can be monitored, invasively and/or non-invasively,depending on the nature of the anticipated physiological state of thepatient. In an aspect, monitoring the one or more physical parametersmight comprise receiving, e.g., from a physiological sensor, continuouswaveform data, which can be sampled as necessary. Such data can include,without limitation, plethysmograph waveform data, PPG waveform data(such as that generated by a pulse oximeter), and/or the like.

The method 200 might further comprise analyzing, with a computer system(e.g., a monitoring computer 100 and/or a processing unit 135 of asensor unit, as described above), the physiological data (block 215). Insome cases, the physiological data is analyzed against a pre-existingmodel (which might be generated as described above and which in turn,can be updated based on the analysis, as described in further detailbelow and in the Related Applications).

Merely by way of example, in some cases, sensor data can be analyzeddirectly against a generated model to assess the effectiveness ofhydration (which can include estimating current values, and/orpredicting future values for any or all of BL, V, and/or Pf, asexpressed above. For example, the sensor data can be compared todetermine similarities with models that estimate and/or predict any ofthese values. Merely by way of example, an input waveform captured by asensor from a patient might be compared with sample waveforms generatedby models for each of these values.

For example, the technique 200′ provides one method for deriving anestimate of BL in accordance with some embodiments. It should be notedthat the technique 200′ is presented as an example only, and that whilethis technique 200′ estimates BL from raw sensor data, similartechniques can be used to estimate or predict BL, V, and/or P_(ƒ) fromraw sensor data, CRI data, and/or a combination of both. For example,one model might produce a first estimate of BL from raw sensor data,produce a second estimate of BL from estimated CRI values, and thencombine those estimates (in either weighted or unweighted fashion) toproduce a hybrid BL estimate.

The illustrated technique 200′ comprises sampling waveform data (e.g.,any of the data described herein and in the Related Applications,including without limitation arterial waveform data, such as continuousPPG waveforms and/or continuous noninvasive blood pressure waveforms)for a specified period, such as 32 heartbeats (block 270). That sampleis compared with a plurality of waveforms of reference datacorresponding to BL values (block 275), which in this case range from 0to 1 using the scale described above (but alternatively might use anyappropriate scale). These reference waveforms are derived as part of themodel developed using the algorithms described in this and the RelatedApplications, might be the result of experimental data, and/or the like.In effect, these reference waveforms reflect the relationship ƒ from Eq.6, above.

According to the technique 200′, the sample might be compared withwaveforms corresponding to a BL=1 (block 275 a), BL=0.5 (block 275 b),and BL=0 (block 275 c), as illustrated. (As illustrated by the ellipsesin FIG. 2B, any number of sample waveforms can be used for thecomparison; for example, if there is a nonlinear relationship betweenthe measured sensor data and the BL values, more sample waveforms mightprovide for a better comparison.) From the comparison, a similaritycoefficient is calculated (e.g., using a least squares or similaranalysis) to express the similarity between the sampled waveform andeach of the reference waveforms (block 280). These similaritycoefficients can be normalized (if appropriate) (block 285), and thenormalized coefficients can be summed (block 290) to produce anestimated BL value of the patient (block 295).

In other cases, similar techniques can be used to analyze data against amodel based on parameters derived from direct sensor measurements. (Inone aspect, such operations can be iterative in nature, by generatingthe derived parameters—such as CRI, to name one example—by analyzing thesensor data against a first model, and then analyzing the derivedparameters against a second model.

For example, FIG. 3A illustrates a method 300 of calculating a patient'sCRI, which can be used (in some embodiments) as a parameter that can beanalyzed to assess the effectiveness of hydration (including theprobability that fluids are needed and/or the estimated volume of fluidnecessary for effective hydration) and/or to assess blood loss (e.g.,before, during, and/or after fluid resuscitation). The method 300includes generating a model of CRI (block 305), monitoring physiologicalparameters (310) and analyzing the monitored physical parameters (block315), using techniques such as those described above and in the '483application, for example.

Based on this analysis, the method 300, in an exemplary embodiment,includes estimating, with the computer system, a compensatory reserve ofthe patient, based on analysis of the physiological data (block 320). Insome cases, the method might further comprise predicting, with thecomputer system, the compensatory reserve of the patient at one or moretime points in the future, based on analysis of the physiological data(block 325). The operations to predict a future value of a parameter canbe similar to those for estimating a current value; in the predictioncontext, however, the applied model might correlate measured data in atest subject with subsequent values of the diagnostic parameter, ratherthan contemporaneous values. It is worth noting, of course, that in someembodiments, the same model can be used to both estimate a current valueand predict future values of a physiological parameter.

The estimated and/or predicted compensatory reserve of the patient canbe based on several factors. Merely by way of example, in some cases,the estimated/predicted compensatory reserve can be based on a fixedtime history of monitoring the physiological data of the patient and/ora dynamic time history of monitoring the physiological data of thepatient. In other cases, the estimated/predicted compensatory reservecan be based on a baseline estimate of the patient's compensatoryreserve established when the patient is euvolemic. In still other cases,the estimate and/or prediction might not be based on a baseline estimateof the patient's compensatory reserve established when the patient iseuvolemic.

Merely by way of example, FIG. 3B illustrates one technique 300′ forderiving an estimate of CRI in accordance with some embodiments similarto the technique 200′ described above with respect to FIG. 2B forderiving an assessment of hydration effectiveness and/or deriving anassessment of blood loss (e.g., before, during, and/or after fluidresuscitation) directly from sensor data (and, in fact, CRI can bederived as described herein, and that derived value can be used, aloneor with raw sensor data, to assess such effectiveness). The illustratedtechnique comprises sampling waveform data (e.g., any of the datadescribed herein and in the Related Applications, including, withoutlimitation, arterial waveform data, such as continuous PPG waveformsand/or continuous noninvasive blood pressure waveforms) for a specifiedperiod, such as 32 heartbeats (block 370). That sample is compared witha plurality of waveforms of reference data corresponding to differentCRI values (block 375). (These reference waveforms might be derivedusing the algorithms described in the Related Applications, might be theresult of experimental data, and/or the like). Merely by way of example,the sample might be compared with waveforms corresponding to a CRI of 1(block 375 a), a CRI of 0.5 (block 375 b), and a CRI of 0 (block 375 c),as illustrated. From the comparison, a similarity coefficient iscalculated (e.g., using a least squares or similar analysis) to expressthe similarity between the sampled waveform and each of the referencewaveforms (block 380). These similarity coefficients can be normalized(if appropriate) (block 385), and the normalized coefficients can besummed (block 390) to produce an estimated value of the patient's CRI(block 395).

Returning to FIG. 3A, the method 300 can comprise estimating and/orpredicting a patient's dehydration state (block 330). The patient'sstate of dehydration can be expressed in a number of ways. For instance,the state of dehydration might be expressed as a normalized value (forexample, with 1.0 corresponding to a fully hydrated state and 0.0corresponding to a state of morbid dehydration). In other cases, thestate of dehydration might be expressed as a missing volume of fluid oras a volume of fluid present in the patient's system, or using any otherappropriate metric.

A number of techniques can be used to model dehydration state. Merely byway of example, as noted above (and described in further detail below),the relationship between a patient's compensatory reserve and level ofdehydration can be modeled. Accordingly, in some embodiments, estimatinga dehydration state of the patient might comprise estimating thecompensatory reserve (e.g., CRI) of the patient, and then, based on thatestimate and the known relationship, estimating the dehydration state.Similarly, a predicted value of compensatory reserve at some point inthe future can be used to derive a predicted dehydration state at thatpoint in the future. Other techniques might use a parameter other thanCRI to model dehydration state.

The method 300 might further comprise normalizing the results of theanalysis (block 335), such as the compensatory reserve, dehydrationstate, and/or probability of bleeding, to name a few examples. Merely byway of example, the estimated/predicted compensatory reserve of thepatient can be normalized relative to a normative normal blood volumevalue corresponding to euvolemia, a normative excess blood volume valuecorresponding to circulatory overload, and a normative minimum bloodvolume value corresponding to cardiovascular collapse. Any values can beselected as the normative values. Merely by way of example, in someembodiments, the normative excess blood volume value is >1, thenormative normal blood volume value is 1, and the normative minimumblood volume value is 0. As an alternative, in other embodiments, thenormative excess blood volume value might be defined as 1, the normativenormal blood volume value might be defined as 0, and the normativeminimum blood volume value at the point of cardiovascular collapse mightbe defined as −1. As can be seen from these examples, differentembodiments might use a number of different scales to normalize CRI andother estimated parameters.

In an aspect, normalizing the data can provide benefits in a clinicalsetting, because it can allow the clinician to quickly make aqualitative judgment of the patient's condition, while interpretation ofthe raw estimates/predictions might require additional analysis. Merelyby way of example, with regard to the estimate of the compensatoryreserve of the patient, that estimate might be normalized relative to anormative normal blood volume value corresponding to euvolemia and anormative minimum blood volume value corresponding to cardiovascularcollapse. Once again, any values can be selected as the normativevalues. For example, if the normative normal blood volume is defined as1, and the normative minimum blood volume value is defined as 0, thenormalized value, falling between 0.0 and 1.0 can quickly apprise aclinician of the patient's location on a continuum between euvolemia andcardiovascular collapse. Similar normalizing procedures can beimplemented for other estimated data (such as probability of bleeding,dehydration, and/or the like).

The method 300 might further comprise displaying data with a displaydevice (block 340). Such data might include an estimate and/orprediction of the compensatory reserve of the patient and/or an estimateand/or prediction of the patient's dehydration state. A variety oftechniques can be used to display such data. Merely by way of example,in some cases, displaying the estimate of the compensatory reserve ofthe patient might comprise displaying the normalized estimate of thecompensatory reserve of the patient. Alternatively and/or additionally,displaying the normalized estimate of the compensatory reserve of thepatient might comprise displaying a graphical plot showing thenormalized excess blood volume value, the normalized normal blood volumevalue, the normalized minimum blood volume value, and the normalizedestimate of the compensatory reserve (e.g., relative to the normalizedexcess blood volume value, the normalized normal blood volume value, thenormalized minimum blood volume value).

In some cases, the method 300 might comprise repeating the operations ofmonitoring physiological data of the patient, analyzing thephysiological data, and estimating (and/or predicting) the compensatoryreserve of the patient, to produce a new estimated (and/or predicted)compensatory reserve of the patient. Thus, displaying the estimate(and/or prediction) of the compensatory reserve of the patient mightcomprise updating a display of the estimate of the compensatory reserveto show the new estimate (and/or prediction) of the compensatoryreserve, in order to display a plot of the estimated compensatoryreserve over time. Hence, the patient's compensatory reserve can berepeatedly estimated and/or predicted on any desired interval (e.g.,after every heartbeat), on demand, before fluid resuscitation, duringfluid resuscitation, after fluid resuscitation, etc., or a combinationof one or more of these.

In further embodiments, the method 300 can comprise determining aprobability that the patient is bleeding, and/or displaying, with thedisplay device, an indication of the probability that the patient isbleeding (block 345). For example, some embodiments might generate amodel based on data that removes fluid from the circulatory system (suchas LBNP, dehydration, etc.). Another embodiment might generate a modelbased on fluid removed from a subject voluntarily, e.g., during a blooddonation, based on the known volume (e.g., 500 cc) of the donation.Based on this model, using techniques similar to those described above,a patient's physiological data can be monitored and analyzed to estimatea probability that the patient is bleeding (e.g., internally).

In some cases, the probability that the patient is bleeding can be usedto adjust the patient's estimated CRI. Specifically, give a probabilityof bleeding expressed as Pr_Bleed at a time t, the adjusted value of CRIcan be expressed as:

CRI _(Adjusted)(t)=1−((1−CRI(t))×Pr_Bleed(t))  (Eq. 13)

Given this relationship, the estimated CRI can be adjusted to produce amore accurate diagnosis of the patient's condition at a given point intime.

The method 300 might comprise selecting, with the computer system, arecommended treatment option for the patient, and/or displaying, withthe display device, the recommended treatment option (block 355). Therecommended treatment option can be any of a number of treatmentoptions, including, without limitation, optimizing hemodynamics of thepatient, a ventilator adjustment, an intravenous fluid adjustment,transfusion of blood or blood products to the patient, infusion ofvolume expanders to the patient, a change in medication administered tothe patient, a change in patient position, and surgical therapy, or thelike.

In a specific, non-limiting, example, the method 300 might comprisecontrolling operation of hemodialysis equipment (block 360), based atleast in part on the estimate of the patient's compensatory reserve.Merely by way of example, a computer system that performs the monitoringand estimating functions might also be configured to adjust anultra-filtration rate of the hemodialysis equipment in response to theestimated CRI values of the patient. In other embodiments, the computersystem might provide instructions or suggestions to a human operator ofthe hemodialysis equipment, such as instructions to manually adjust anultra-filtration rate, etc.

In some embodiments, the method 300 might include assessing thetolerance of an individual to blood loss, general volume loss, and/ordehydration (block 365). For example, such embodiments might includeestimating a patient's CRI based on the change in a patient's position(e.g., from lying prone to standing, from standing to lying prone, fromlying prone to sitting, from sitting to lying prone, from standing tositting, and/or from sitting to standing). Based on changes to thepatient's CRI in response to these maneuvers, the patient's sensitivityto blood loss, volume loss, and/or dehydration can be measured. In anaspect, this measurement can be performed using a CRI model generated asdescribed above; the patient can be monitored using one or more of thesensors described above, and the changes in the sensor output when thesubject changes position can be analyzed according to the model (asdescribed above, for example) to assess the tolerance of the individualto volume loss. Such monitoring and/or analysis can be performed in realtime.

Returning to FIG. 2 , based on the analysis of the data (whether datacollected directly by sensors or derived data, such as CRI, or both)against a model (which might include multiple sub-models, such as amodel of BL against raw data and a model of BL against CRI), the method200 can include assessing the blood loss of the patient (block 220),based on analysis of the patient's physiological data against the model.As noted above, assessing blood loss can include estimating orpredicting a number of values, such as the estimated effectiveness, BL,of the hydration effort, the volume, V, of fluid necessary for effectivehydration, the probability, Pf, that the patient needs fluids, and/orthe like.

In some cases, the assessment of the blood loss will be based on theanalysis of a plurality of measured (or derived) values of a particularphysiological parameter (or plurality of parameters). Hence, in somecases, the analysis of the data might be performed on a continuouswaveform, either during or after measurement of the waveform with asensor (or both), and the assessment of the blood loss can be updated ashydration efforts and/or fluid resuscitation efforts continue. Further,the amount of fluids added to the patient's blood volume can be measureddirectly, and these direct measurements can be fed back into the modelto update the model (at block 225) and thereby improve performance ofthe algorithms in the model (e.g., by refining the weights given todifferent parameters in terms of estimative or predictive value). Theupdated model can then be used to continue assessing the treatment (inthe instant patient and/or in a future patient), as shown by the brokenlines on FIG. 2A.

In some cases, the method 200 comprises displaying data (block 230)indicating the assessment of the effectiveness of hydration. In somecases, the data might be displayed on a display of a sensor device (suchas the device 105 illustrated by FIG. 1B). Alternatively and/oradditionally the data might be displayed on a dedicated machine, such asa compensatory reserve monitor, or on a monitor of a generic computersystem. The data might be displayed alphanumerically, graphically, orboth. FIGS. 6-8 , described below, illustrate several possible exemplarydisplays of assessments of blood loss and/or CRI. There are manydifferent ways that the data can be displayed, and any assessments,estimates or predictions generated by the method 200 can be displayed inany desired way, in accordance with various embodiments.

In certain embodiments, the method 200 can include selecting and/ordisplaying treatment options for the patient (block 235) and/orcontrolling a therapeutic device (block 240), based on the assessment ofthe blood loss of the patient. For example, a display might indicate toa clinician or the patient him or herself that the patient is losing (orhas lost) blood, that fluid resuscitation therapy should be initiated orcontinued, an estimated volume of fluid to drink, infuse, or otherwiseconsume, a drip rate for an IV drip, a flow rate for an IV pump orinfuser, or the like. Similarly, the system might be configured tocontrol operation of a therapeutic device, such as dispensing a fluid todrink from an automated dispenser, activating or adjusting the flow rateof an IV pump or infuser, adjusting the drip rate of an IV drip, and/orthe like, based on the assessment of the effectiveness of hydration. Asanother example, certain embodiments might include a water bladder(e.g., a backpack-based hydration pack, such as those available fromCamelbak Products LLC) or a water bottle, and the hydration monitorcould communicate with and/or control operation of such a dispensingdevice (e.g., to cause the device to dispense a certain amount of fluid,to cause the device to trigger an audible alarm, etc.).

Further, in certain embodiments, the method 200 can includefunctionality to help a clinician (or other entity) to monitorhydration, fluid resuscitation, and/or blood volume status. For example,in some cases, any measure of effectiveness outside of the normal range(such as a value of P_(ƒ) higher than a certain threshold value, a valueof BL lower than a threshold value, etc.) would set off various alarmconditions, such as an audible alarm, a message to a physician, amessage to the patient, an update written automatically to a patient'schart, etc. Such messaging could be accomplished by electronic mail,text message, etc., and a sensor device or monitoring computer could beconfigured with, e.g., an SMTP client, text messaging client, or thelike to perform such messaging.

In some cases, feedback and/or notifications might be sent to a thirdparty, regardless of whether any alarm condition were triggered. Forexample, a hydration monitor might be configured to send monitoringresults (e.g., any of the assessments, estimates and/or predictionsdescribed herein) to another device or computer, either for personalmonitoring by the patient or for monitoring by another. Examples couldinclude transmitting such alarms or data (e.g., by Bluetooth, NFC, WiFi,etc.) to a wireless phone, wearable device (e.g., smart watch orglasses) or other personal device of the patient, e.g., for inclusion ina health monitoring application. Additionally and/or alternatively, suchinformation could be sent to a specified device or computer (e.g., viaany available IP connection), for example to allow a parent to monitor achild's (or a child to monitor an elderly parent's) hydration remotely,to allow a coach to monitor a player's hydration remotely, and/or toallow a superior officer to monitor a soldier's hydration remotely, orthe like. In some cases (e.g., for a coach or superior officer), anapplication might aggregate results from a plurality of hydrationmonitors, to allow the supervisor to view (e.g., in a dashboard-typeconfiguration), hydration effectiveness and/or blood loss (and/or anyother data, such as CRI, blood pressure, etc.) for a group of people.Such a display might employ, for example, a plurality of “fuel gauge”displays, one (or more) for each person in the group, allowing thesupervisor to quickly ascertain any unusual results (e.g., based on thecolor of the gauge, etc.).

Similarly, if an alarm condition were met for another physiologicalparameter (such as blood pressure, which can be estimated as describedin the '171 application, for example), that alarm could trigger anassessment of hydration effectiveness via the method 200, to determinewhether the first alarm condition has merit or not. If not, perhapsthere could be an automated silencing of the original alarm condition,since all is well at present. More generally, the assessment techniquescould be added to an ecosystem of monitoring algorithms (including,without limitation, those described in the Related Applications), whichwould inform one another or work in combination, to inform one anotherabout how to maintain optimal physiological stability.

FIG. 4 illustrates a method 400 of employing such a self-learningpredictive model (or machine learning) technique, according to someembodiments. In particular, the method 400 can be used to correlatephysiological data received from a subject sensor with a measuredphysiological state. More specifically, with regard to variousembodiments, the method 400 can be used to generate a model forassessing, predicting and/or estimating various physiologicalparameters, such as blood loss volume, effectiveness of hydration orfluid resuscitation efforts, estimated and/or predicted blood pressure,CRI, the probability that a patient is bleeding, a patient's dehydrationstate, and/or the like, from one or more of a number of differentphysiological parameters, including without limitation those describedabove and in the Related Applications.

The method 400 begins at block 405 by collecting raw data measurementsthat may be used to derive a set of D data signals s₁, . . . , s_(D) asindicated at block 410 (each of the data signals s being, in aparticular case, input from one or many different physiologicalsensors). Embodiments are not constrained by the type of measurementsthat are made at block 405 and may generally operate on any data set.For example, data signals can be retrieved from a computer memory and/orcan be provided from a sensor or other input device. As a specificexample, the data signals might correspond to the output of the sensorsdescribed above (which measure the types of waveform data describedabove, such as continuous, non-invasive PPG data and/or blood pressurewaveform data).

A set of K current or future outcomes {right arrow over (o)}=(o₁, . . .o_(K)) is hypothesized at block 415 (the outcomes o being, in this case,past and/or future physiological states, such as probability that fluidsare needed, volume of fluid needed for effective hydration or fluidresuscitation, BL, CRI, dehydration state, probability of bleeding,etc.). The method autonomously generates a predictive model M thatrelates the derived data signals {right arrow over (s)} with theoutcomes {right arrow over (o)}. As used herein, “autonomous” means“without human intervention.”

As indicated at block 420, this is achieved by identifying the mostpredictive set of signals S_(k), where S_(k) contains at least some (andperhaps all) of the derived signals s₁, . . . , s_(D) for each outcomeo_(k), where k∈{1, . . . , K}. A probabilistic predictive modelô_(k)=M_(k)(S_(k)) is learned at block 425, where ô_(k) is theprediction of outcome o_(k) derived from the model M_(k) that uses asinputs values obtained from the set of signals S_(k), for all k∈{1, . .. , K}. The method 400 can learn the predictive modelsô_(k)−M_(k)(S_(k)) incrementally (block 430) from data that containsexample values of signals s₁, . . . , S_(D) and the correspondingoutcomes o₁, . . . , o_(K). As the data become available, the method 400loops so that the data are added incrementally to the model for the sameor different sets of signals S_(k), for all k∈{1, . . . , K}.

While the description above outlines the general characteristics of themethods, additional features are noted. A linear model framework may beused to identify predictive variables for each new increment of data. Ina specific embodiment, given a finite set of data of signals andoutcomes {({right arrow over (s)}₁, {right arrow over (o)}₁), ({rightarrow over (s)}₂, {right arrow over (o)}₂), . . . }, a linear model maybe constructed that has the form, for all k∈{1, . . . , K},

ô _(k)=ƒ_(k)(a ₀+Σ_(i=1) ^(d) a _(i) s _(i))  (Eq. 14)

where ƒ_(k) is any mapping from one input to one output, and a₀, a₁, . .. , a_(d) are the linear model coefficients. The framework used toderive the linear model coefficients may estimate which signals s, s₁, .. . , s_(d) are not predictive and accordingly sets the correspondingcoefficients a₀, a₁, . . . , a_(d) to zero. Using only the predictivevariables, the model builds a predictive density model of the data,{({right arrow over (s)}₁, {right arrow over (o)}₁), ({right arrow over(s)}₂, {right arrow over (o)}₂), . . . }. For each new increment ofdata, a new predictive density models can be constructed.

In some embodiments, a prediction system can be implemented that canpredict future results from previously analyzed data using a predictivemodel and/or modify the predictive model when data does not fit thepredictive model. In some embodiments, the prediction system can makepredictions and/or to adapt the predictive model in real-time. Moreover,in some embodiments, a prediction system can use large data sets notonly to create the predictive model, but also predict future results aswell as adapt the predictive model.

In some embodiments, a self-learning, prediction device can include adata input, a processor, and an output. Memory can include applicationsoftware that when executed can direct the processor to make aprediction from input data based on a predictive model. Any type ofpredictive model can be used that operates on any type of data. In someembodiments, the predictive model can be implemented for a specific typeof data. In some embodiments, when data is received the predictive modelcan determine whether it understands the data according to thepredictive model. If the data is understood, a prediction is made andthe appropriate output provided based on the predictive model. If thedata is not understood when received, then the data can be added to thepredictive model to modify the model. In some embodiments, the devicecan wait to determine the result of the specified data and can thenmodify the predictive model accordingly. In some embodiments, if thedata is understood by the predictive model and the output generatedusing the predictive model is not accurate, then the data and theoutcome can be used to modify the predictive model. In some embodiments,modification of the predictive model can occur in real-time.

Particular embodiments can employ the tools and techniques described inthe Related Applications in accordance with the methodology describedherein perform the functions of a cardiac reserve monitor, awrist-wearable sensor device, and/or a monitoring computer, as describedherein (the functionality of any or all of which can be combined in asingle, integrated device, in some embodiments). These functionsinclude, but are not limited to, assessing fluid resuscitation of apatient, assessing hydration of a patient, monitoring, estimating and/orpredicting a subject's (including, without limitation, a patient's)current or future blood pressure and/or compensatory reserve, estimatingand/or determining the probability that a patient is bleeding (e.g.,internally) and/or has been bleeding, recommending treatment options forsuch conditions, and/or the like. Such tools and techniques include, inparticular, the systems (e.g., computer systems, sensors, therapeuticdevices, etc.) described in the Related Applications, the methods (e.g.,the analytical methods for generating and/or employing analyticalmodels, the diagnostic methods, etc.), and the software programsdescribed herein and in the Related Applications, which are incorporatedherein by reference.

FIG. 5 illustrates a method 500 of implementing rapid detection ofbleeding before, during, and after fluid resuscitation, in accordancewith various embodiments. In the embodiment of FIG. 5 , method 500, atblock 505, comprises estimating a patient's CRI before, during, and/orafter resuscitation (e.g., fluid resuscitation, or the like). Estimationof the patient's CRI may be performed, for example, using the techniquesdescribed above with respect to FIGS. 3A and 3B, or using othertechniques described above.

At block 510, method 500 might comprise recording the patient's CRI,before, during, and/or after resuscitation. In some instances, the CRImay be recorded or stored on one or more of a data storage device thatis part of processing unit 145 and/or a memory device that is part ofthe monitoring computer 100 of FIG. 1 , or the like. Method 500 mightfurther comprise calculating an average CRI over a period of K seconds(where K>1), before, during, and/or after resuscitation (block 515),calculating a standard deviation or variance of CRI over a period of Kseconds (where K>1), before, during, and/or after resuscitation (block520), calculating Pearson's moment coefficient of skewness of CRI over aperiod of K seconds (where K>1), before, during, and/or afterresuscitation (block 525), calculating a rate of change of CRI over aperiod of K seconds (where K>1), before, during, and/or afterresuscitation (block 530), calculating a rate of rate change (or a rateof change of rate change) of CRI (also referred to herein as“acceleration of CRI”) over a period of K seconds (where K>1), before,during, and/or after resuscitation (block 535).

According to some embodiments, method 500 might further comprise, atblock 540, determining probability of bleeding, based on one or more ofthe calculations in blocks 515-535 (which may be referred to herein as“variation results”). In other words, the variation results might beused to estimate one or more states of bleeding—namely, a (certain)non-bleeding state (perhaps designated by a symbol, “0”), a (certain)bleeding state (perhaps designated by a symbol, “1”), and someprobability of bleeding state (perhaps designated by a symbol between“0” and “1”).

In some embodiments, the following definitions might be used for (i) CRIvalue sample, (ii) a set of values of CRI, (iii) average CRI, (iv)median CRI, (v) standard deviation of CRI, (vi) rate of change of CRI,(vii) rate of change of rate change of CRI, and (viii) skewness of CRI:

-   -   (i) A specific CRI value at time t:

CRI(t);  (Eq. 15)

-   -   (ii) A set of CRI values at times {t₁, t₂, . . . , t_(K)}:

CRI={CRI(t ₁),CRI(t ₂), . . . ,CRI(t _(K))};  (Eq. 16)

-   -   (iii) Average CRI value over a specific set of times {t₁, t₂, .        . . , t_(K)}:

CRI _(K)Σ_(k=1) ^(K) CRI(t _(k));  (Eq. 17)

-   -   (iv) Median CRI value over a specific set of times {t₁, t₂, . .        . , t_(K)}:

CRI _(K) ^(Med)=Median{CRI(t ₁),CRI(t ₂), . . . ,CRI(t _(K))};  (Eq. 18)

-   -   (v) A measure of deviation of CRI over a specific set of times        {t₁, t₂, . . . , t_(K)}, perhaps variance, or standard deviation        defined by:

$\begin{matrix}{{{{SD}\left( {CRI_{K}} \right)} = \sqrt{\frac{{\Sigma}_{k = 1}^{K}\left( {{CR{I\left( t_{k} \right)}} - {\overset{\_}{C⁢R⁢I}}_{K}} \right)^{2}}{K}}};} & \left( {{Eq}.19} \right)\end{matrix}$

-   -   (vi) Rate of change of CRI, denoted by m_(K), over a set of CRI        values {CRI(t₁), CRI(t₂), . . . , CRI(t_(K))}, where the rate of        change measures some increase or decrease of CRI over a specific        period of time, and, for example, may be calculated as a slope        of the line:

$\begin{matrix}{{\begin{bmatrix}m_{K} \\b\end{bmatrix} = {\left( {A^{t}A} \right)^{- 1}{A^{t}\begin{bmatrix}\begin{matrix}{CR{I\left( t_{1} \right)}} \\ \vdots \end{matrix} \\{CR{I\left( t_{K} \right)}}\end{bmatrix}}}},} & \left( {{Eq}.20} \right)\end{matrix}$

-   -   where A is a matrix defined by:

$\begin{matrix}{{A = \begin{bmatrix}t_{1} & 1 \\ \vdots & \vdots \\t_{K} & 1\end{bmatrix}};} & \left( {{Eq}.21} \right)\end{matrix}$

-   -   (vii) Rate of change of rate change of CRI, denoted by r_(K),        over a set of CRI values {CRI(t₁), CRI(t₂), . . . , CRI(t_(K))},        where the rate of change of rate change measures some rate of        change of increase or decrease of CRI over a specific period of        time, and, for example, may be calculated as a second order        increase or decrease of a curve:

$\begin{matrix}{{\begin{bmatrix}r_{K} \\m_{K} \\b\end{bmatrix} = {\left( {B^{t}B} \right)^{- 1}{B^{t}\begin{bmatrix}\begin{matrix}{CR{I\left( t_{1} \right)}} \\ \vdots \end{matrix} \\{CR{I\left( t_{K} \right)}}\end{bmatrix}}}},} & \left( {{Eq}.22} \right)\end{matrix}$

-   -   where B is a matrix defined by:

$\begin{matrix}{{B = \begin{bmatrix}\left( t_{1} \right)^{2} & t_{1} & 1 \\ \vdots & \vdots & \vdots \\\left( t_{k} \right)^{2} & t_{k} & 1\end{bmatrix}};} & \left( {{Eq}.23} \right)\end{matrix}$

-   -   (viii) Some measure of skewness, denoted by S_(K) (not to be        confused with set of signals, S_(k), as described above with        respect to FIG. 4 ), over a set of CRI values {CRI(t₁), CRI(t₂),        . . . , CRI(t_(K))}, where S_(K) is possibly a variant of the        Fisher-Pearson coefficient of skewness:

$\begin{matrix}{{S_{K} = {\frac{1}{\left( {S{D\left( {CRI_{K}} \right)}} \right)^{3}}\left\lbrack \frac{{\Sigma}_{k = 1}^{K}\left( {{CR{I\left( t_{k} \right)}} - {\overset{\_}{C⁢R⁢I}}_{K}} \right)^{3}}{K} \right\rbrack}},} & \left( {{Eq}.24} \right)\end{matrix}$

-   -   and/or S_(K) is some other measure of skewness, possibly Galton        skewness (or Bowley's skewness), as defined as:

$\begin{matrix}{{S_{K} = \frac{Q_{1} + Q_{3} - {2Q_{2}}}{Q_{3} - Q_{1}}}.} & \left( {{Eq}.25} \right)\end{matrix}$

A method for estimating a (certain) non-bleeding state might include,but is not limited to, one of the following calculations or acombination of two or more such calculations, perhaps within astatistical and/or machine learning framework, or the like: (1) Averageof CRI before resuscitation (“CRI _(BR)”)>NB1; (2) Average of CRI duringresuscitation (“CRI _(DR)”)>NB2; (3) Average of CRI after resuscitation(“CRI _(AR)”)>NB3; (4) CRI _(AR)−CRI _(DR)>NB4; (5) CRI _(DR)−CRI_(BR)>NB5; (6) CRI _(AR)−CRI _(BR)>NB6; (7) standard deviation orvariance of CRI before resuscitation (“[SD(CRI)]_(BR)”)<NB7; (8)standard deviation or variance of CRI during resuscitation(“[SD(CRI)]_(DR)”)<NB8; (9) standard deviation or variance of CRI afterresuscitation (“[SD(CRI)]_(AR)”)<NB9; (10) [SD(CRI)]_(AR)−[SD(CRI)]_(BR)<NB10; (11) moment coefficient of skewness of CRI(positive or negative) before resuscitation (“S_(BR)”)<NB11; (12) momentcoefficient of skewness of CRI (positive or negative) duringresuscitation (“S_(DR)”)<NB12; (13) moment coefficient of skewness ofCRI (positive or negative) after resuscitation (“S_(AR)”)<NB13; (14)rate of change of CRI before resuscitation (“m_(BR)”)>NB14; (15) rate ofchange of CRI during resuscitation (“m_(DR)”)>NB15; (16) rate of changeof CRI after resuscitation (“m_(AR)”)>NB16; (17) m_(AR)−m_(BR)>NB17;(18) m_(DR)−m_(BR)>NB18; (19) rate of rate change of CRI beforeresuscitation (“r_(BR)”)>NB19; (20) rate of rate change of CRI duringresuscitation (“r_(DR)”)>NB20; (21) rate of rate change of CRI afterresuscitation (“r_(AR)”)>NB21; (22) r_(AR)−r_(BR)>NB22; (23)r_(DR)−r_(BR)>NB23; and/or the like. In some cases, each of, or one ormore of, NB1 through NB23 might either be estimated experimentally orset by the user. Herein, the number K>0 may be different in eachinstance of the calculations (1) through (23), may be chosen by theuser, or may be experimentally determined.

With reference to (1) the average CRI before resuscitation,CRI_(BR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times before resuscitation, and CRI_(BR) may be the averagevalue of those points. Accordingly, for example, a classification of nobleeding may be made by choosing a threshold, either experimentally oruser set, denoted by ^(NB) CRI _(BR) (e.g., NB1 above), and classifyingnon-bleeding may be determined if:

CRI _(BR)>^(NB) CRI _(BR).  (Eq. 26)

Referring to (2) the average CRI during resuscitation,CRI_(DR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times during resuscitation, and CRI _(DR) may be the averagevalue of those points. Accordingly, a classification of no bleeding maybe made by choosing a threshold, either experimentally or user set,denoted by ^(NB) CRI _(DR) (e.g., NB2 above), and classifyingnon-bleeding may be determined if:

CRI _(DR)>^(NB) CRI _(DR).  (Eq.27)

Regarding (3) the average CRI after resuscitation, CRI_(AR)={CRI(t₁),CRI(t₂), . . . , CRI(t_(K))} may be any set of points sampled at timesafter resuscitation, and CRI _(AR) may be the average value of thosepoints. Accordingly, a classification of no bleeding may be made bychoosing a threshold, either experimentally or user set, denoted by^(NB) CRI _(AR) (e.g., NB3 above), and classifying non-bleeding may bedetermined if:

CRI _(AR)>^(NB) CRI _(AR).  (Eq. 28)

With reference to (4), CRI _(DR) and CRI _(AR) may be as defined above.Accordingly, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by _(AR) ^(NB) CRI_(DR) (e.g., NB4 above), and classifying non-bleeding may be determinedif:

CRI _(AR) −CRI _(DR)>_(AR) ^(NB) CRI _(DR).  (Eq. 29)

Referring to (5), CRI _(BR) and CRI _(DR) may be as defined above.Accordingly, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by _(DR) ^(NB) CRI_(BR) (e.g., NB5 above), and classifying non-bleeding may be determinedif:

CRI _(DR) −CRI _(BR)>_(DR) ^(NB) CRI _(BR).  (Eq. 30)

Regarding (6), CRI _(BR) and CRI _(AR) may be as defined above.Accordingly, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by _(AR) ^(NB) CRI_(BR) (e.g., NB6 above), and classifying non-bleeding may be determinedif:

CRI _(AR) −CRI _(BR)>_(AR) ^(NB) CRI _(BR).  (Eq. 31)

With reference to (7) the variance of CRI before resuscitation,CRI_(BR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times before resuscitation, and [SD(CRI)]_(BR) may be thevariation of those values (perhaps the standard deviation as definedabove). Accordingly, for example, a classification of no bleeding may bemade by choosing a threshold, either experimentally or user set, denotedby ^(NB)[SD(CRI)]_(BR) (e.g., NB7 above), and classifying non-bleedingmay be determined if:

[SD(CRI)]_(BR)<^(NB) [SD(CRI)]_(BR).  (Eq. 32)

Referring to (8) the variance of CRI during resuscitation,CRI_(DR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times during resuscitation, and [SD(CRI)]_(DR) may be thevariation of those values (perhaps the standard deviation as definedabove). Accordingly, a classification of no bleeding may be made bychoosing a threshold, either experimentally or user set, denoted by^(NB)[SD(CRI)]_(DR) (e.g., NB8 above), and classifying non-bleeding maybe determined if:

[SD(CRI)]_(DR)<^(NB) [SD(CRI)]_(DR).  (Eq. 33)

Regarding (9) the variance of CRI after resuscitation,CRI_(AR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times after resuscitation, and [SD(CRI)]_(AR) may be thevariation of those values (perhaps the standard deviation as definedabove). Accordingly, a classification of no bleeding may be made bychoosing a threshold, either experimentally or user set, denoted by^(NB)[SD(CRI)]_(AR) (e.g., NB9 above), and classifying non-bleeding maybe determined if:

[SD(CRI)]_(AR)<^(NB) [SD(CRI)]_(AR).  (Eq. 34)

Referring to (10), [SD(CRI)]_(BR) and [SD(CRI)]_(AR) may be as definedabove. Accordingly, a classification of no bleeding may be made bychoosing a threshold, either experimentally or user set, denoted by_(AR) ^(NB)[SD(CRI)]_(BR) (e.g., NB10 above), and classifyingnon-bleeding may be determined if:

[SD(CRI)]_(AR) −[SD(CRI)]_(BR)<_(AR) ^(NB) [SD(CRI)]_(BR).  (Eq. 35)

With reference to (11) the skewness of CRI before resuscitation,CRI_(BR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times before resuscitation, and S_(BR) may be a measure ofskewness of those points (perhaps as defined above). Accordingly, forexample, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by ^(NB)S_(BR)(e.g., NB11 above), and classifying non-bleeding may be determined if:

|S _(BR)|<^(NB) S _(BR).  (Eq. 36)

Referring to (12) the skewness of CRI during resuscitation,CRI_(DR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times during resuscitation, and S_(DR) may be a measure ofskewness of those points (perhaps as defined above). Accordingly, aclassification of no bleeding may be made by choosing a threshold,either experimentally or user set, denoted by ^(NB)S_(DR) (e.g., NB12above), and classifying non-bleeding may be determined if:

|S _(DR)|<^(NB) S _(DR).  (Eq. 37)

Regarding (13) the skewness of CRI after resuscitation,CRI_(AR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times after resuscitation, and S_(AR) may be a measure ofskewness of those points (perhaps as defined above). Accordingly, aclassification of no bleeding may be made by choosing a threshold,either experimentally or user set, denoted by ^(NB)S_(AR) (e.g., NB13above), and classifying non-bleeding may be determined if:

|S _(AR)|<^(NB) S _(AR).  (Eq. 38)

With reference to (14) the rate of change of CRI before resuscitation,CRI_(BR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times before resuscitation, and m_(BR) may be a measure ofrate of change of those points (perhaps as defined above). Accordingly,for example, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by ^(NB)m_(BR)(e.g., NB14 above), and classifying non-bleeding may be determined if:

m _(BR)>^(NB) m _(BR).  (Eq. 39)

Referring to (15) the rate of change of CRI during resuscitation,CRI_(DR) {CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times during resuscitation, and m_(DR) may be a measure ofrate of change of those points (perhaps as defined above). Accordingly,a classification of no bleeding may be made by choosing a threshold,either experimentally or user set, denoted by ^(NB)m_(DR) (e.g., NB15above), and classifying non-bleeding may be determined if:

m _(DR)>^(NB) m _(DR).  (Eq. 40)

Regarding (16) the rate of change of CRI after resuscitation,CRI_(AR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times after resuscitation, and m_(AR) may be a measure ofrate of change of those points (perhaps as defined above). Accordingly,a classification of no bleeding may be made by choosing a threshold,either experimentally or user set, denoted by ^(NB)m_(AR) (e.g., NB16above), and classifying non-bleeding may be determined if:

m _(AR)>^(NB) m _(AR).  (Eq. 41)

With reference to (17), m_(BR) and m_(AR) may be as defined above.Accordingly, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by _(AR)^(NB)m_(BR) (e.g., NB17 above), and classifying non-bleeding may bedetermined if:

m _(AR) −m _(BR)>^(AR) m _(BR).  (Eq. 42)

Referring to (18), m_(BR) and m_(DR) may be as defined above.Accordingly, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by _(DR)^(NB)m_(BR) (e.g., NB18 above), and classifying non-bleeding may bedetermined if:

m _(DR) −m _(BR)>_(DR) ^(NB) m _(BR).  (Eq. 43)

With reference to (19) the rate of rate change of CRI beforeresuscitation, CRI_(BR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may beany set of points sampled at times before resuscitation, and r_(BR) maybe a measure of rate of rate change of those points (perhaps as definedabove). Accordingly, for example, a classification of no bleeding may bemade by choosing a threshold, either experimentally or user set, denotedby ^(NB)r_(BR) (e.g., NB19 above), and classifying non-bleeding may bedetermined if:

r _(BR)>^(NB) r _(BR).  (Eq. 44)

Referring to (20) the rate of rate change of CRI during resuscitation,CRI_(DR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times during resuscitation, and r_(DR) may be a measure ofrate of rate change of those points (perhaps as defined above).Accordingly, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by ^(NB)r_(DR)(e.g., NB20 above), and classifying non-bleeding may be determined if:

r _(DR)>^(NB) r _(DR).  (Eq. 45)

Regarding (21) the rate of rate change of CRI after resuscitation,CRI_(AR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times after resuscitation, and r_(AR) may be a measure ofrate of rate change of those points (perhaps as defined above).Accordingly, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by ^(NB)r_(AR)(e.g., NB21 above), and classifying non-bleeding may be determined if:

r _(AR)>^(NB) r _(AR).  (Eq. 46)

With reference to (22), r_(BR) and r_(AR) may be as defined above.Accordingly, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by _(AR)^(NB)r_(BR) (e.g., NB22 above), and classifying non-bleeding may bedetermined if:

r _(AR) −r _(BR)>_(AR) ^(NB) r _(BR).  (Eq. 47)

Referring to (23), m_(BR) and m_(DR) may be as defined above.Accordingly, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by _(DR)^(NB)m_(BR) (e.g., NB23 above), and classifying non-bleeding may bedetermined if:

r _(DR) −r _(BR)>_(DR) ^(NB) r _(BR).  (Eq. 48)

Similarly, in some instances, a method for estimating a (certain)bleeding state might include, but is not limited to, one of thefollowing calculations or a combination of two or more suchcalculations, perhaps within a statistical and/or machine learningframework, or the like: (1) Average of CRI before resuscitation (“CRI_(BR)”)<BL1; (2) Average of CRI during resuscitation (“CRI _(DR)”)<BL2;(3) Average of CRI after resuscitation (“CRI _(AR)”)<BL3; (4) CRI_(AR)−CRI _(DR)<BL4; (5) CRI _(DR)−CRI _(BR)<BL5; (6) CRI _(AR)−CRI_(BR)<BL6; (7) standard deviation of CRI before resuscitation(“[SD(CRI)]_(BR)”)>BL7; (8) standard deviation of CRI duringresuscitation (“[SD(CRI)]_(DR)”)>BL8; (9) standard deviation of CRIafter resuscitation (“[SD(CRI)]_(AR)”)>BL9; (10)[SD(CRI)]_(AR)−[SD(CRI)]_(BR)>BL10; (11) moment coefficient of skewnessof CRI (positive or negative) before resuscitation (“S_(BR)”)>BL11; (12)moment coefficient of skewness of CRI (positive or negative) duringresuscitation (“S_(DR)”)>BL12; (13) moment coefficient of skewness ofCRI (positive or negative) after resuscitation (“S_(AR)”)>BL13; (14)rate of change of CRI before resuscitation (“m_(BR)”)<BL14; (15) rate ofchange of CRI during resuscitation (“m_(DR)”)<BL15; (16) rate of changeof CRI after resuscitation (“m_(AR)”)<BL16; (17) m_(AR)−m_(BR)<BL17;(18) m_(DR)−m_(BR)<BL18; (19) rate of rate change of CRI beforeresuscitation (“r_(BR)”)<BL19; (20) rate of rate change of CRI duringresuscitation (“r_(DR)”)<BL20; (21) rate of rate change of CRI afterresuscitation (“r_(AR)”)<BL21; (22) r_(AR)−r_(BR)<BL22; (23)r_(DR)−r_(BR)<BL23; and/or the like. In some cases, each of, or one ormore of, BL1 through BL20 might either be estimated experimentally orset by the user.

With reference to (1) the average CRI before resuscitation,CRI_(BR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times before resuscitation, and CRI _(BR) may be the averagevalue of those points. Accordingly, for example, a classification of nobleeding may be made by choosing a threshold, either experimentally oruser set, denoted by ^(B) CRI _(BR) (e.g., BL1 above), and classifyingnon-bleeding may be determined if:

CRI _(BR)<^(B) CRI _(BR).  (Eq. 49)

Referring to (2) the average CRI during resuscitation,CRI_(DR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times during resuscitation, and CRI _(DR) may be the averagevalue of those points. Accordingly, a classification of no bleeding maybe made by choosing a threshold, either experimentally or user set,denoted by ^(B) CRI _(DR) (e.g., BL2 above), and classifyingnon-bleeding may be determined if:

CRI _(DR)<^(B) CRI _(DR).  (Eq. 50)

Regarding (3) the average CRI after resuscitation, CRI_(AR)={CRI(t₁),CRI(t₂), . . . , CRI(t_(K))} may be any set of points sampled at timesafter resuscitation, and CRI _(AR) may be the average value of thosepoints. Accordingly, a classification of no bleeding may be made bychoosing a threshold, either experimentally or user set, denoted by ^(B)CRI _(AR) (e.g., BL3 above), and classifying non-bleeding may bedetermined if:

CRI _(AR)<^(B) CRI _(AR).  (Eq. 51)

With reference to (4), CRI _(DR) and CRI _(AR) may be as defined above.Accordingly, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by _(AR) ^(B) CRI_(DR) (e.g., BL4 above), and classifying non-bleeding may be determinedif:

CRI _(AR) −CRI _(DR)<_(AR) ^(B) CRI _(DR).  (Eq. 52)

Referring to (5), CRI _(BR) and CRI _(DR) may be as defined above.Accordingly, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by _(DR) ^(B) CRI_(BR) (e.g., BL5 above), and classifying non-bleeding may be determinedif:

CRI _(DR) −CRI _(BR)<_(DR) ^(B) CRI _(BR).  (Eq. 53)

Regarding (6), CRI _(BR) and CRI _(AR) may be as defined above.Accordingly, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by _(AR) ^(B) CRI_(BR) (e.g., BL6 above), and classifying non-bleeding may be determinedif:

CRI _(AR) −CRI _(BR)<_(AR) ^(B) CRI _(BR).  (Eq. 54)

With reference to (7) the variance of CRI before resuscitation,CRI_(BR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times before resuscitation, and [SD(CRI)]_(BR) may be thevariation of those values (perhaps the standard deviation as definedabove). Accordingly, for example, a classification of no bleeding may bemade by choosing a threshold, either experimentally or user set, denotedby ^(B)[SD(CRI)]_(BR) (e.g., BL7 above), and classifying non-bleedingmay be determined if:

[SD(CRI)]_(BR)>^(B) [SD(CRI)]_(BR).  (Eq. 55)

Referring to (8) the variance of CRI during resuscitation, CRI_(DR){CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of points sampledat times during resuscitation, and [SD(CRI)]_(DR) may be the variationof those values (perhaps the standard deviation as defined above).Accordingly, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by^(B)[SD(CRI)]_(DR) (e.g., BL8 above), and classifying non-bleeding maybe determined if:

[SD(CRI)]_(DR)>^(B) [SD(CRI)]_(DR).  (Eq. 56)

Regarding (9) the variance of CRI after resuscitation,CRI_(AR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times after resuscitation, and [SD(CRI)]_(AR) may be thevariation of those values (perhaps the standard deviation as definedabove). Accordingly, a classification of no bleeding may be made bychoosing a threshold, either experimentally or user set, denoted by^(B)[SD(CRI)]_(AR) (e.g., BL9 above), and classifying non-bleeding maybe determined if:

[SD(CRI)]_(AR)>^(B) [SD(CRI)]_(AR).  (Eq. 57)

Referring to (10), [SD(CRI)]_(BR) and [SD(CRI)]_(AR) may be as definedabove. Accordingly, a classification of no bleeding may be made bychoosing a threshold, either experimentally or user set, denoted by_(AR) ^(B)[SD(CRI)]_(BR) (e.g., BL10 above), and classifyingnon-bleeding may be determined if:

[SD(CRI)]_(AR) −[SD(CRI)]_(BR)>_(AR) ^(B) [SD(CRI)]_(BR).  (Eq. 58)

With reference to (11) the skewness of CRI before resuscitation,CRI_(BR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times before resuscitation, and S_(BR) may be a measure ofskewness of those points (perhaps as defined above). Accordingly, forexample, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by ^(B)S_(BR)(e.g., BL11 above), and classifying non-bleeding may be determined if:

|S _(BR)|>^(B) S _(BR).  (Eq. 59)

Referring to (12) the skewness of CRI during resuscitation,CRI_(DR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times during resuscitation, and S_(DR) may be a measure ofskewness of those points (perhaps as defined above). Accordingly, aclassification of no bleeding may be made by choosing a threshold,either experimentally or user set, denoted by ^(B)S_(DR) (e.g., BL12above), and classifying non-bleeding may be determined if:

|S _(DR)|>^(B) S _(DR).  (Eq. 60)

Regarding (13) the skewness of CRI after resuscitation,CRI_(AR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times after resuscitation, and S_(AR) may be a measure ofskewness of those points (perhaps as defined above). Accordingly, aclassification of no bleeding may be made by choosing a threshold,either experimentally or user set, denoted by ^(B)S_(AR) (e.g., BL13above), and classifying non-bleeding may be determined if:

|S _(AR)|>^(B) S _(AR).  (Eq. 61)

With reference to (14) the rate of change of CRI before resuscitation,CRI_(BR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times before resuscitation, and m_(BR) may be a measure ofrate of change of those points (perhaps as defined above). Accordingly,for example, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by ^(B)m_(BR)(e.g., BL14 above), and classifying non-bleeding may be determined if:

m _(BR)<^(B) m _(BR).  (Eq. 62)

Referring to (15) the rate of change of CRI during resuscitation,CRI_(DR) {CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times during resuscitation, and m_(DR) may be a measure ofrate of change of those points (perhaps as defined above). Accordingly,a classification of no bleeding may be made by choosing a threshold,either experimentally or user set, denoted by ^(B)m_(DR) (e.g., BL15above), and classifying non-bleeding may be determined if:

m _(DR)<^(B) m _(DR).  (Eq. 63)

Regarding (16) the rate of change of CRI after resuscitation,CRI_(AR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times after resuscitation, and m_(AR) may be a measure ofrate of change of those points (perhaps as defined above). Accordingly,a classification of no bleeding may be made by choosing a threshold,either experimentally or user set, denoted by ^(B)m_(AR) (e.g., BL16above), and classifying non-bleeding may be determined if:

m _(AR)<^(B) m _(AR).  (Eq. 64)

With reference to (17), m_(BR) and m_(AR) may be as defined above.Accordingly, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by _(AR)^(B)m_(BR) (e.g., BL17 above), and classifying non-bleeding may bedetermined if:

m _(AR) −m _(BR)<_(AR) ^(B) m _(BR).  (Eq. 65)

Referring to (18), m_(BR) and m_(DR) may be as defined above.Accordingly, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by _(DR)^(B)m_(BR) (e.g., BL18 above), and classifying non-bleeding may bedetermined if:

m _(DR) −m _(BR)<_(DR) ^(B) m _(BR).  (Eq. 66)

With reference to (19) the rate of rate change of CRI beforeresuscitation, CRI_(BR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may beany set of points sampled at times before resuscitation, and r_(BR) maybe a measure of rate of rate change of those points (perhaps as definedabove). Accordingly, for example, a classification of no bleeding may bemade by choosing a threshold, either experimentally or user set, denotedby ^(B)r_(BR) (e.g., BL19 above), and classifying non-bleeding may bedetermined if:

r _(BR)<^(B) r _(BR).  (Eq. 67)

Referring to (20) the rate of rate change of CRI during resuscitation,CRI_(DR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times during resuscitation, and r_(DR) may be a measure ofrate of rate change of those points (perhaps as defined above).Accordingly, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by ^(B)r_(DR)(e.g., BL20 above), and classifying non-bleeding may be determined if:

r _(DR)<^(B) r _(DR).  (Eq. 68)

Regarding (21) the rate of rate change of CRI after resuscitation,CRI_(AR)={CRI(t₁), CRI(t₂), . . . , CRI(t_(K))} may be any set of pointssampled at times after resuscitation, and r_(AR) may be a measure ofrate of rate change of those points (perhaps as defined above).Accordingly, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by ^(B)r_(AR)(e.g., BL21 above), and classifying non-bleeding may be determined if:

r _(AR)<^(B) r _(AR).  (Eq. 69)

With reference to (22), r_(BR) and r_(AR) may be as defined above.Accordingly, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by _(AR)^(B)r_(BR) (e.g., BL22 above), and classifying non-bleeding may bedetermined if:

r _(AR) −r _(BR)<_(AR) ^(B) r _(BR).  (Eq. 70)

Referring to (23), m_(BR) and m_(DR) may be as defined above.Accordingly, a classification of no bleeding may be made by choosing athreshold, either experimentally or user set, denoted by _(DR)^(B)m_(BR) (e.g., BL23 above), and classifying non-bleeding may bedetermined if:

r _(DR) −r _(BR)<_(DR) ^(B) r _(BR).  (Eq. 71)

Likewise, in some instances, a method for estimating a probability ofbleeding (e.g., designated by a symbol between 0 and 1) might include,but is not limited to, one of the above calculations or a combination oftwo or more such calculations, perhaps within a statistical and/ormachine learning framework, or the like, to estimate the probability ofbleeding. In some cases, the method might include, without limitation,empirical estimations of probability density functions, cumulativedistribution functions using graphical and/or nonparametric models,and/or the like. Other methods might include, but are not limited to:(i) probability of bleeding being proportional to the number of timesthe bleeding threshold is achieved; (ii) probability of no bleedingbeing proportional to the number of times the no bleeding threshold isachieved; (iii) probability of bleeding being proportional to the numberof times the bleeding threshold is achieved minus the number of timesthe no bleeding threshold is achieved; (iv) probability of bleedingbeing expressed as

Pr(bleeding)=ƒ( CRI _(BR) ,CRI _(DR) ,CRI _(AR) ,[SD(CRI)]_(BR),[SD(CRI)]_(DR) ,[SD(CRI)]_(AR) ,S _(BR) ,S _(DR) ,S _(AR) ,m _(BR) ,m_(DR) ,m _(AR) ,r _(BR) ,r _(DR) ,r _(AR)),  (Eq. 72)

where ƒ is some empirical estimation of the probability density functionand/or cumulative distribution functions using graphical and/ornonparametric models.

In some embodiments, estimated CRI values might include, but are notlimited to, one or more of CRI values estimated or measured after everyheartbeat, CRI values averaged over the preceding or last N seconds(where N>1), and/or the median value of CRI over the preceding or last Nseconds (where N>1), or the like. According to some embodiments, thecalculations described above with respect to blocks 515-535 mightutilize these estimated CRI values. According to some embodiments,instead of using CRI measurements, a method might use all or some of thecalculations above that replace CRI values with values corresponding tomeasurements related to any measure of compensatory reserve, orderivative thereof, using one or more of the sensor types describedabove.

FIGS. 6-8 illustrate exemplary screen captures from a display device ofa compensatory reserve monitor, showing various features that can beprovided by one or more embodiments. Similar screens could be shown byother monitoring devices, such as a display of a wrist-wearable sensordevice, a display of a monitoring computer, and/or the like. While FIGS.6-8 use BL or CRI as an example condition for illustrative purposes,other embodiments might also display values for the volume, V, thevolume of fluid necessary for effective hydration, or the probability,Pf, that the patient needs fluid (including additional fluid, ifhydration efforts already are underway).

FIG. 6 illustrates an exemplary display 600 of a compensatory reservemonitor implementation where a normalized CRI estimate of “1” impliesthat blood loss is certain, and “0” implies that there is no blood loss.Values in between “0” and “1” imply a continuum of a probability ofblood loss.

FIG. 7A illustrates four screen captures 700 of a display of acompensatory reserve monitor implementation that displays BL as a “fuelgauge” type bar graph for a person undergoing central volume blood lossand subsequent hydration efforts, or for a person who is about to, isundergoing, or has undergone fluid resuscitation. While FIG. 6illustrates a trace of CRI over time, the bar graphs of FIG. 7A providesnapshots of BL at the time of each screen capture corresponding to theCRI of FIG. 6 . (In the illustrated implementation, the bar graphs arecontinuously and/or periodically updated, such that each bar graph couldcorrespond to a particular position on the X-axis of FIG. 6 .)

A variety of additional features are possible. Merely by way of exampleFIG. 7B illustrates similar “fuel gauge” type displays, but the displaysfeature bars of different colors—for example, green (illustrated bydiagonal cross-hatching), yellow (illustrated by a checked pattern) andred illustrated by gray shading) corresponding to different levels ofCRI, along with arrows 710 indicating trending in the CRI values (e.g.,rising, declining, or remaining stable), the CRI values and trends beingindicative of blood loss occurring and/or resuscitation efforts beingactive.

In some embodiments, such a “fuel gauge” display (or other indicator ofBL or CRI and/or different physiological parameters) can be incorporatedin a more comprehensive user interface. Merely by way of example, FIG. 8illustrates an exemplary display 800 of a monitoring system. The display800 includes a graphical, color-coded “fuel gauge” type display 805 ofthe current estimated BL (similar to the displays illustrated by FIG.8B), along with a historical display 810 of recent CRI estimates; inthis example, each bar on the historical display 810 might correspond toan estimate performed every minute, but different estimate frequenciesare possible, and in some embodiments, the operator can be given theoption to specify a different frequency. In the illustrated embodiment,the display 800 also includes numerical display 815 of the current BL aswell as a trend indicator 820 (similar to that indicated above).

In particular embodiments, the display 800 can include additionalinformation (and, in some cases, the types of information displayedand/or the type of display can be configured by the operator). Forinstance, the exemplary display 800 includes an indicator 825 of thepatient's current heart rate and an indicator 830 of the patient's bloodoxygen saturation level (SpO2). The exemplary display 800 also includesan indicator of the estimated volume, V, necessary for effectivehydration, as well as an numerical indicator 840, a trend indicator 845,and a similar color coded “fuel gauge” display 850 of the current CRIOther monitored parameters might be displayed as well, such as an ECGtracing, blood pressure, probability of bleeding estimates, and/or thelike.

Exemplary Clinical Study

FIGS. 9A-9H (collectively, “FIG. 9 ”) are graphical diagrams 900illustrating rapid detection of bleeding before, during, and after fluidresuscitation of patients in a multi-trauma clinical study at DenverHealth Medical Center (“DHMC”), in accordance with various embodiments.In one exemplary multi-trauma clinical study at DHMC, 50 patients wereenrolled, of which 45 patients met required criteria while 5 wereexcluded (as having incomplete data and/or device). Of the 45 patients,12 were bleeding (with initial CRI values of 0.17±0.07 and mean injuryseverity score (“ISS”) of 27±12.7), 30 non-bleeding (with initial CRIvalues of 0.56±0.17 and mean ISS of 7.5±8.7), and 3 indeterminate.

With reference to FIG. 9 , FIG. 9A illustrates a receiver operatingcharacteristic (“ROC”) curve that is used for classification of bleedingusing compensatory reserve. The sensitivity is 0.93, with specificity of0.92, and area under the curve (“AUC”) of 0.97.

FIG. 9B illustrates the CRI for the non-bleeding patients (indicated inthe graph as “Trauma No Hemorrhage”) and for the bleeding patients(indicated in the graph as “Trauma+Hemorrhage”). As shown in FIG. 9B,CRI values are low during bleeding.

FIGS. 9C-9E illustrate line tracings of actual CRI curves for threerepresentative patients among the non-bleeding group. The CRI values forthe non-bleeding patients before infusing of intravenous fluid (“IVF”)is 0.56±0.17. FIG. 9C depicts the CRI curves for non-bleeding traumapatient 003, who had a CRI of >0.3 before infusion of IVF, and with IVFcontaining 2 L of saline solution. There was no sustained drop in CRI inthis patient during or after infusion of IVF. FIG. 9D depicts the CRIcurves for non-bleeding trauma patient 042, who had a CRI of 0.4 beforeinfusion of IVF, and with IVF containing 1 L of saline solution. Therewas no wound exploration and no sustained drop in CRI in this patientduring or after infusion of IVF. FIG. 9E depicts the CRI curves fornon-bleeding trauma patient 018, who had a CRI of 0.65 before infusionof IVF, and with IVF containing 2 L of saline solution, 1 L of lactatedringer's (“LR”) solution, and 2 packets of packed red blood cells(“PRBC”). There was no sustained drop in CRI in this patient during orafter infusion of IVF. As shown in FIGS. 9C-9E, CRI is high or generallyincreasing during and after fluid resuscitation for the non-bleedinggroup.

FIGS. 9F-9H illustrate line tracings of actual CRI curves for threerepresentative patients among the bleeding group. The CRI values for thenon-bleeding patients before infusing of intravenous fluid (“IVF”) is0.17±0.07. FIG. 9F depicts the CRI curves for bleeding trauma patient019, who had a CRI of 0.15 before infusion of IVF (at time 905), andwith an infusion of a first IVF (at time 910), the first IVF containing7 L of saline solution, 3 packets of PRBC, 1 packet of platelets(“PLTs”), and 3 packets of fresh frozen plasma (“FFP”). The CRI droppedafter initial increase (as shown at time 915). At time 920, a second IVFwas infused, the second IVF containing 4 L of saline solution, 3 packetsof PRBC, and 3 packets of fresh frozen plasma (“FFP”). FIG. 9G depictsthe CRI curves for bleeding trauma patient 006, who had a CRI of 0.15before infusion of IVF (at time 925), and with an infusion of a firstIVF (at time 930), the first IVF containing 2 L of saline solution. TheCRI dropped after initial increase (as shown at time 935). At time 940,a second IVF was infused, the second IVF containing 1 L of salinesolution. Again, the CRI dropped (as shown at time 945). FIG. 9H depictsthe CRI curves for bleeding trauma patient 012, who had a CRI of 0.15before infusion of IVF, with infusions of a first IVF (at time 950) anda second IVF (at time 955), the first IVF containing 1 L of salinesolution and the second IVF containing 2.25 L of saline solution. Asshown in FIGS. 9F-9H, CRI drops after an initial increase (during andafter fluid resuscitation) for the bleeding group.

Exemplary System and Hardware Implementation

FIG. 10 is a block diagram illustrating an exemplary computer or systemhardware architecture, in accordance with various embodiments. FIG. 10provides a schematic illustration of one embodiment of a computer system1000 that can perform the methods provided by various other embodiments,as described herein, and/or can function as a monitoring computer, a CRImonitor, a processing unit of a sensor device, and/or the like, asdescribed above. It should be noted that FIG. 10 is meant only toprovide a generalized illustration of various components, of which oneor more (or none) of each may be utilized as appropriate. FIG. 10 ,therefore, broadly illustrates how individual system elements may beimplemented in a relatively separated or relatively more integratedmanner.

The computer or hardware system 1000 is shown comprising hardwareelements that can be electrically coupled via a bus 1005 (or mayotherwise be in communication, as appropriate). The hardware elementsmay include one or more processors 1010, including, without limitation,one or more general-purpose processors and/or one or morespecial-purpose processors (such as digital signal processing chips,graphics acceleration processors, and/or the like); one or more inputdevices 1015, which can include, without limitation, a mouse, a keyboardand/or the like; and one or more output devices 1020, which can include,without limitation, a display device, a printer, and/or the like.

The computer or hardware system 1000 may further include (and/or be incommunication with) one or more storage devices 1025, which cancomprise, without limitation, local and/or network accessible storage,and/or can include, without limitation, a disk drive, a drive array, anoptical storage device, solid-state storage device such as a randomaccess memory (“RAM”) and/or a read-only memory (“ROM”), which can beprogrammable, flash-updateable, and/or the like. Such storage devicesmay be configured to implement any appropriate data stores, including,without limitation, various file systems, database structures, and/orthe like.

The computer or hardware system 1000 might also include a communicationssubsystem 1030, which can include, without limitation, a modem, anetwork card (wireless or wired), an infra-red communication device, awireless communication device and/or chipset (such as a Bluetooth™device, an 802.11 device, a WiFi device, a WiMax device, a WWAN device,cellular communication facilities, etc.), and/or the like. Thecommunications subsystem 1030 may permit data to be exchanged with anetwork (such as the network described below, to name one example), withother computer or hardware systems, and/or with any other devicesdescribed herein. In many embodiments, the computer or hardware system1000 will further comprise a working memory 1035, which can include aRAM or ROM device, as described above.

The computer or hardware system 1000 also may comprise softwareelements, shown as being currently located within the working memory1035, including an operating system 1040, device drivers, executablelibraries, and/or other code, such as one or more application programs1045, which may comprise computer programs provided by variousembodiments (including, without limitation, hypervisors, VMs, and thelike), and/or may be designed to implement methods, and/or configuresystems, provided by other embodiments, as described herein. Merely byway of example, one or more procedures described with respect to themethod(s) discussed above might be implemented as code and/orinstructions executable by a computer (and/or a processor within acomputer); in an aspect, then, such code and/or instructions can be usedto configure and/or adapt a general purpose computer (or other device)to perform one or more operations in accordance with the describedmethods.

A set of these instructions and/or code might be encoded and/or storedon a non-transitory computer readable storage medium, such as thestorage device(s) 1025 described above. In some cases, the storagemedium might be incorporated within a computer system, such as thesystem 1000. In other embodiments, the storage medium might be separatefrom a computer system (i.e., a removable medium, such as a compactdisc, etc.), and/or provided in an installation package, such that thestorage medium can be used to program, configure, and/or adapt a generalpurpose computer with the instructions/code stored thereon. Theseinstructions might take the form of executable code, which is executableby the computer or hardware system 1000 and/or might take the form ofsource and/or installable code, which, upon compilation and/orinstallation on the computer or hardware system 1000 (e.g., using any ofa variety of generally available compilers, installation programs,compression/decompression utilities, etc.) then takes the form ofexecutable code.

It will be apparent to those skilled in the art that substantialvariations may be made in accordance with specific requirements. Forexample, customized hardware (such as programmable logic controllers,field-programmable gate arrays, application-specific integratedcircuits, and/or the like) might also be used, and/or particularelements might be implemented in hardware, software (including portablesoftware, such as applets, etc.), or both. Further, connection to othercomputing devices such as network input/output devices may be employed.

As mentioned above, in one aspect, some embodiments may employ acomputer or hardware system (such as the computer or hardware system1000) to perform methods in accordance with various embodiments of theinvention. According to a set of embodiments, some or all of theprocedures of such methods are performed by the computer or hardwaresystem 1000 in response to processor 1010 executing one or moresequences of one or more instructions (which might be incorporated intothe operating system 1040 and/or other code, such as an applicationprogram 1045) contained in the working memory 1035. Such instructionsmay be read into the working memory 1035 from another computer readablemedium, such as one or more of the storage device(s) 1025. Merely by wayof example, execution of the sequences of instructions contained in theworking memory 1035 might cause the processor(s) 1010 to perform one ormore procedures of the methods described herein.

The terms “machine readable medium” and “computer readable medium,” asused herein, refer to any medium that participates in providing datathat causes a machine to operate in a specific fashion. In an embodimentimplemented using the computer or hardware system 1000, various computerreadable media might be involved in providing instructions/code toprocessor(s) 1010 for execution and/or might be used to store and/orcarry such instructions/code (e.g., as signals). In manyimplementations, a computer readable medium is a non-transitory,physical, and/or tangible storage medium. In some embodiments, acomputer readable medium may take many forms, including, but not limitedto, non-volatile media, volatile media, or the like. Non-volatile mediaincludes, for example, optical and/or magnetic disks, such as thestorage device(s) 1025. Volatile media includes, without limitation,dynamic memory, such as the working memory 1035. In some alternativeembodiments, a computer readable medium may take the form oftransmission media, which includes, without limitation, coaxial cables,copper wire and fiber optics, including the wires that comprise the bus1005, as well as the various components of the communication subsystem1030 (and/or the media by which the communications subsystem 1030provides communication with other devices). In an alternative set ofembodiments, transmission media can also take the form of waves(including, without limitation, radio, acoustic and/or light waves, suchas those generated during radio-wave and infra-red data communications).

Common forms of physical and/or tangible computer readable mediainclude, for example, a floppy disk, a flexible disk, a hard disk,magnetic tape, or any other magnetic medium, a CD-ROM, any other opticalmedium, punch cards, paper tape, any other physical medium with patternsof holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chipor cartridge, a carrier wave as described hereinafter, or any othermedium from which a computer can read instructions and/or code.

Various forms of computer readable media may be involved in carrying oneor more sequences of one or more instructions to the processor(s) 1010for execution. Merely by way of example, the instructions may initiallybe carried on a magnetic disk and/or optical disc of a remote computer.A remote computer might load the instructions into its dynamic memoryand send the instructions as signals over a transmission medium to bereceived and/or executed by the computer or hardware system 1000. Thesesignals, which might be in the form of electromagnetic signals, acousticsignals, optical signals, and/or the like, are all examples of carrierwaves on which instructions can be encoded, in accordance with variousembodiments of the invention.

The communications subsystem 1030 (and/or components thereof) generallywill receive the signals, and the bus 1005 then might carry the signals(and/or the data, instructions, etc. carried by the signals) to theworking memory 1035, from which the processor(s) 1005 retrieves andexecutes the instructions. The instructions received by the workingmemory 1035 may optionally be stored on a storage device 1025 eitherbefore or after execution by the processor(s) 1010.

CONCLUSION

This document discloses novel tools and techniques for blood loss inpatients (e.g., before, during, and/or after fluid resuscitation),compensatory reserve, and similar physiological states. While certainfeatures and aspects have been described with respect to exemplaryembodiments, one skilled in the art will recognize that numerousmodifications are possible. For example, the methods and processesdescribed herein may be implemented using hardware components, softwarecomponents, and/or any combination thereof. Further, while variousmethods and processes described herein may be described with respect toparticular structural and/or functional components for ease ofdescription, methods provided by various embodiments are not limited toany particular structural and/or functional architecture but instead canbe implemented on any suitable hardware, firmware and/or softwareconfiguration. Similarly, while certain functionality is ascribed tocertain system components, unless the context dictates otherwise, thisfunctionality can be distributed among various other system componentsin accordance with the several embodiments.

Moreover, while the procedures of the methods and processes describedherein are described in a particular order for ease of description,unless the context dictates otherwise, various procedures may bereordered, added, and/or omitted in accordance with various embodiments.Moreover, the procedures described with respect to one method or processmay be incorporated within other described methods or processes;likewise, system components described according to a particularstructural architecture and/or with respect to one system may beorganized in alternative structural architectures and/or incorporatedwithin other described systems. Hence, while various embodiments aredescribed with—or without—certain features for ease of description andto illustrate exemplary aspects of those embodiments, the variouscomponents and/or features described herein with respect to a particularembodiment can be substituted, added and/or subtracted from among otherdescribed embodiments, unless the context dictates otherwise.Consequently, although several exemplary embodiments are describedabove, it will be appreciated that the invention is intended to coverall modifications and equivalents within the scope of the followingclaims.

1.-35. (canceled)
 36. A monitoring device, comprising: one or more input devices configured to receive raw waveform data of a patient; one or more processing circuits configured to: monitor the raw waveform data corresponding to cardiovascular state of the patient; analyze, using a compensatory reserve model, the raw waveform data to generate a physiological estimation of the patient based on a subset of signals of a set of signals of the raw waveform data; and provide the physiological estimation of the patient.
 37. The monitoring device of claim 36, wherein the one or more processing circuits select the subset of signals of the set of signals based on a determination of predictiveness corresponding to blood loss or an effectiveness of a treatment, wherein the determination of predictiveness comprises a likelihood of cardiovascular collapse or near-cardiovascular collapse.
 38. The monitoring device of claim 37, wherein the subset of signals corresponds to identified predicative variables the compensatory reserve model used during model building, wherein the one or more processing circuits identify the identified predictive variables based on a linear or non-linear model framework.
 39. The monitoring device of claim 36, wherein each signal of the subset of signals corresponds to a signal value, wherein the one or more processing circuits perform learning of the compensatory reserve model based on example values of signals corresponding with one or more outcomes.
 40. The monitoring device of claim 36, wherein the subset of signals of the raw waveform data is correlated with one or more physiological state measurements, and wherein the one or more physiological state measurements comprises at least one of a state of blood loss or fluid resuscitation.
 41. The monitoring device of claim 36, wherein the physiological estimation of the patient is a compensatory reserve value measuring a hemodynamic state of the patient over a particular period of time, and wherein the raw waveform data corresponding to the cardiovascular state of the patient is continuously collected over the particular period of time.
 42. The monitoring device of claim 36, the one or more processing circuits are further configured to: analyze, using the compensatory reserve model, the raw waveform data to generate a future physiological estimation of the patient.
 43. The monitoring device of claim 36, wherein the compensatory reserve model is learned on the subset of signals of the set of signals and correlations between lower body negative pressure (LBNP) data, wherein one or more test subjects are subjected to LBNP during the learning.
 44. A method, comprising: receiving, by the one or more processing circuits, raw waveform data of a patient; monitoring, by the one or more processing circuits, the raw waveform data corresponding to cardiovascular state of the patient; analyzing, by the one or more processing circuits using a compensatory reserve model, the raw waveform data to generate a physiological estimation of the patient based on a subset of signals of a set of signals of the raw waveform data; and providing, by the one or more processing circuits, the physiological estimation of the patient.
 45. The method of claim 44, wherein the one or more processing circuits select the subset of signals of the set of signals based on a determination of predictiveness corresponding to blood loss or an effectiveness of a treatment, wherein the determination of predictiveness comprises a likelihood of cardiovascular collapse or near-cardiovascular collapse.
 46. The method of claim 45, wherein the subset of signals corresponds to identified predicative variables the compensatory reserve model used during model building, wherein the one or more processing circuits identify the identified predictive variables based on a linear or non-linear model framework.
 47. The method of claim 44, wherein each signal of the subset of signals corresponds to a signal value, wherein the one or more processing circuits perform learning of the compensatory reserve model based on example values of signals corresponding with one or more outcomes.
 48. The method of claim 44, wherein the subset of signals of the raw waveform data is correlated with one or more physiological state measurements, and wherein the one or more physiological state measurements comprises at least one of a state of blood loss or fluid resuscitation.
 49. The method of claim 44, wherein the physiological estimation of the patient is a compensatory reserve value measuring a hemodynamic state of the patient over a particular period of time, and wherein the raw waveform data corresponding to the cardiovascular state of the patient is continuously collected over the particular period of time.
 50. The method of claim 44, further comprising: analyzing, by the one or more processing circuits using the compensatory reserve model, the raw waveform data to generate a future physiological estimation of the patient.
 51. The method of claim 44, wherein the compensatory reserve model is learned on the subset of signals of the set of signals and correlations between lower body negative pressure (LBNP) data, wherein one or more test subjects are subjected to LBNP during the learning.
 52. One or more non-transitory computer-readable storage media having instructions stored thereon that, when executed by one or more processing circuits, causes the one or more processing circuits to: receive raw waveform data of a patient; monitor the raw waveform data corresponding to cardiovascular state of the patient; analyze, using a compensatory reserve model, the raw waveform data to generate a physiological estimation of the patient based on a subset of signals of a set of signals of the raw waveform data; and provide the physiological estimation of the patient.
 53. The one or more non-transitory computer-readable storage media of claim 52, wherein the instructions cause the one or more processing circuits to select the subset of signals of the set of signals based on a determination of predictiveness corresponding to blood loss or an effectiveness of a treatment, wherein the determination of predictiveness comprises a likelihood of cardiovascular collapse or near-cardiovascular collapse associated with the cardiovascular state of the patient.
 54. The one or more non-transitory computer-readable storage media of claim 53, wherein the subset of signals corresponds to identified predicative variables the compensatory reserve model used during model building, wherein the instructions cause the one or more processing circuits to identify the identified predictive variables based on a linear or non-linear model framework.
 55. The one or more non-transitory computer-readable storage media of claim 52, wherein each signal of the subset of signals corresponds to a signal value, wherein the instructions cause the one or more processing circuits to perform learning of the compensatory reserve model based on example values of signals corresponding with one or more outcomes. 